262 packages found (Stata Journal omitted) ------------------------------------------ @net:describe sftfe, from(http://www.econometrics.it/stata)!sftfe from http://www.econometrics.it/stata@ sftfe. Consistent estimation of fixed-effects stochastic frontier models (version 1.2.9 19oct2022). / {cmd: sftfe} fits the following fixed-effects stochastic frontier model: / y_it = alpha_i + beta*X_it + v_it {c 177} u_it / where v_it is a normally distributed error term and u_it is a @net:describe art, from(http://www.homepages.ucl.ac.uk/~ucakjpr/stata)!art from http://www.homepages.ucl.ac.uk/~ucakjpr/stata@ ART. Package for complex sample size calculation in randomized trials. / Program by Abdel Babiker, Friederike (Sophie) Barthel and Patrick Royston. / Distribution-Date: 20141223 / Please direct queries to Patrick Royston (j.royston@ucl.ac.uk) / in the first instance. / / To install the ART @net:describe ppssample, from(https://staskolenikov.net/stata)!ppssample from https://staskolenikov.net/stata@ ppssample -- module to perform probability-proporitonal-to-size sampling / Author: Stas Kolenikov, skolenik@@unc.edu / Sorry for the lack of documentation / It requires options -mos()- (measure of size variable) / and -seed- (the seed for the random number generator) / You can also @net:describe annfit, from(https://staskolenikov.net/stata)!annfit from https://staskolenikov.net/stata@ annfit -- Approximation by neural networks / Author: Stas Kolenikov, skolenik@@recep.glasnet.ru / This module performs a version of nonlinear regression / involving a linear part and a neural network part. / Only random search for the best approximating neural / network is implemented @net:describe xtdpdbc, from(http://www.kripfganz.de/stata)!xtdpdbc from http://www.kripfganz.de/stata@ 'XTDPDBC': Bias-corrected estimation of linear dynamic panel models / Sebastian Kripfganz, www.kripfganz.de / xtdpdbc implements the bias-corrected estimator of Breitung, Kripfganz, / and Hayakawa (2021) for linear dynamic panel data models with fixed or / random effects. @net:describe xtdpdqml, from(http://www.kripfganz.de/stata)!xtdpdqml from http://www.kripfganz.de/stata@ 'XTDPDQML': QML estimation of linear dynamic panel models / Sebastian Kripfganz, www.kripfganz.de / xtdpdqml implements the unconditional quasi-maximum likelihood estimators / of Bhargava and Sargan (1983) for linear dynamic panel models with random / effects and Hsiao, Pesaran, and @net:describe gennorm, from(http://www.stata.com/users/wgould)!gennorm from http://www.stata.com/users/wgould@ gennorm. Generate correlated, normal random deviates. / Program by William Gould, Stata Corp . / Statalist distribution, 02 February 1999. / / gennorm creates two or more variables containing correlated normal, / random deviates. @net:describe gendist, from(http://www.stata.com/users/rgutierrez)!gendist from http://www.stata.com/users/rgutierrez@ gendist: Utilities for random number generation. / Utilities for generating data for use with Stata's {cmd:nbreg} (negative / binomial regression) and {cmd:poisson} estimation commands. / This package also contains commands for generating data from the / three-parameter gamma and @net:describe genbinomial, from(http://www.stata.com/users/rgutierrez)!genbinomial from http://www.stata.com/users/rgutierrez@ genbinomial: Generating random deviates from the binomial distribution. / This command generates data which are random draws from a binomial / distribution for a given number of trials n and success probability p. / Both n and p may be specified as either a constant or a variable if / the @net:describe gengammareg, from(http://www.stata.com/users/rgutierrez)!gengammareg from http://www.stata.com/users/rgutierrez@ gengammareg: Generating a time response for {cmd:streg, distribution(gamma)}. / {cmd:gengammareg} generates random deviates from the generalized gamma / distribution using the same parameterization as that used by {cmd:streg} / with option {cmd:distribution(gamma)}. You can @net:describe gllamm6, from(http://www.stata.com/users/jhardin)!gllamm6 from http://www.stata.com/users/jhardin@ gllamm6. Generalized linear latent and mixed models. / Program by Sophia Rabe-Hesketh and Colin Taylor / / Module to estimate generalized linear latent and mixed models. / These models can be thought of as extensions of the simple random / intercept models that may be fitted in Stata using @net:describe rnd, from(http://www.stata.com/users/jhilbe)!rnd from http://www.stata.com/users/jhilbe@ rnd. Random data generators. / Program by Joseph Hilbe, Arizona State Univ. / Statalist distribution, 29 January 1999. / These programs generate random numbers for a variety of important / distributions. This is an update of my STB-28 insert. / See help ^rnd^. @net:describe myrereg, from(http://www.stata.com/users/jpitblado)!myrereg from http://www.stata.com/users/jpitblado@ myrereg: Random effects regression using the -gf2- evaluator / Jeff Pitblado, StataCorp / / ^myrereg^ implements the random effects regression model as described in the / StataPress book about -ml-. In this implementation, I use a -gf2- Mata / function @net:describe sim_arma, from(http://www.stata.com/users/jpitblado)!sim_arma from http://www.stata.com/users/jpitblado@ sim_arma Simulate autoregressive moving average data (version 8) / Jeff Pitblado, StataCorp / / ^sim_arma^ is a random number generator for the autoregressive moving / average model. / ^sim_arma^ was originally developed using Stata 7, but has since been / @net:describe ppssample, from(http://staskolenikov.net/stata)!ppssample from http://staskolenikov.net/stata@ ppssample -- module to perform probability-proporitonal-to-size sampling / Author: Stas Kolenikov, skolenik@@unc.edu / Sorry for the lack of documentation / It requires options -mos()- (measure of size variable) / and -seed- (the seed for the random number generator) / You can also @net:describe annfit, from(http://staskolenikov.net/stata)!annfit from http://staskolenikov.net/stata@ annfit -- Approximation by neural networks / Author: Stas Kolenikov, skolenik@@recep.glasnet.ru / This module performs a version of nonlinear regression / involving a linear part and a neural network part. / Only random search for the best approximating neural / network is implemented @net:describe rndseq, from(http://www.graunt.cat/stata)!rndseq from http://www.graunt.cat/stata@ rndseq. Generation of Random Sequences. / After installation, see help rtrend. / (c)JM. Domenech / Programmer: R. Sesma / Laboratori d'Estadistica Aplicada, Universitat Autonoma de Barcelona. / Distribution-Date: 15dec2022 / Version 1.0.6 (15dec2022) / E-mail: @net:describe _grndraw, from(http://fmwww.bc.edu/RePEc/bocode/_)!_grndraw from http://fmwww.bc.edu/RePEc/bocode/_@ '_GRNDRAW': module for random number generation from the GB2, Singh-Maddala, Dagum, Fisk and Pareto distributions / _grndraw is an egen function for random number generation from / the GB2, Singh-Maddala, Dagum, Fisk and Pareto distributions. / Simulation relies on standard @net:describe addnotes, from(http://fmwww.bc.edu/RePEc/bocode/a)!addnotes from http://fmwww.bc.edu/RePEc/bocode/a@ 'ADDNOTES': program to add notes to the end of text files / Addnotes appends notes to the end of a text file. Addnotes is / useful for adding footnotes to the output of outsheet or outreg. / KW: panel data / KW: random effects / KW: weights / Requires: Stata version 8 / @net:describe allsynth, from(http://fmwww.bc.edu/RePEc/bocode/a)!allsynth from http://fmwww.bc.edu/RePEc/bocode/a@ 'ALLSYNTH': module to automate estimation of (i) bias-corrected synthetic control gaps ("treatment effects") / allsynth is a wrapper for the synth command which automates / the implementation of several additional features. The primary / extensions are: (1) automated estimation of @net:describe ameta, from(http://fmwww.bc.edu/RePEc/bocode/a)!ameta from http://fmwww.bc.edu/RePEc/bocode/a@ 'AMETA': module to perform alternative and Bayesian meta-analysis / ameta offers a choice of two meta-analysis approaches, an / alternative and a bayesian. On the one hand, the alternative / method consists of eight different heterogeneity estimators for / the calculation of the pooled @net:describe art, from(http://fmwww.bc.edu/RePEc/bocode/a)!art from http://fmwww.bc.edu/RePEc/bocode/a@ 'ART': module to compute sample size and power for complex randomised trial designs with binary or time-to-event outcomes / ART is a menu- and command-driven set of programs to compute / sample size or power for randomized controlled trials with a / time-to-event or binary outcome @net:describe artbin, from(http://fmwww.bc.edu/RePEc/bocode/a)!artbin from http://fmwww.bc.edu/RePEc/bocode/a@ 'ARTBIN': module to calculate sample size or power for randomized trials with binary outcomes / artbin is part of the ART suite: Assessment of Resources for / Trials. artbin calculates the power or total sample size for / various tests comparing K anticipated probabilities. Power is / @net:describe artcat, from(http://fmwww.bc.edu/RePEc/bocode/a)!artcat from http://fmwww.bc.edu/RePEc/bocode/a@ 'ARTCAT': module to calculate sample size or power for a two-group trial with ordered categorical outcome / artcat calculates sample size given power, or power given / sample size, for a 2-group randomised controlled trial with an / ordered categorical outcome. Superiority, non-inferiority @net:describe attregtest, from(http://fmwww.bc.edu/RePEc/bocode/a)!attregtest from http://fmwww.bc.edu/RePEc/bocode/a@ 'ATTREGTEST': module to implement the regression-based attrition tests proposed in Ghanem et al. (2022) / attregtest implements the two regression-based attrition tests / proposed in Ghanem et al. (2022). The first test is based on the / testable implication of the identifying @net:describe bcii, from(http://fmwww.bc.edu/RePEc/bocode/b)!bcii from http://fmwww.bc.edu/RePEc/bocode/b@ 'BCII': module to to estimate the number needed to treat (NNT) and confidence intervals for patients improving, or ‘benefiting’ (either improvements gained or deteriorations prevented), in a randomised controlled trial / Estimation of absolute risk reduction and number needed @net:describe bcss, from(http://fmwww.bc.edu/RePEc/bocode/b)!bcss from http://fmwww.bc.edu/RePEc/bocode/b@ 'BCSS': module to create graphs to show how baseline data (prospective or retrospective) affect sample size for a cluster randomised trial / bcss displays graphs examining the impact of varying the amount / of prospective/retrospective baseline data collection on the / number of @net:describe benford, from(http://fmwww.bc.edu/RePEc/bocode/b)!benford from http://fmwww.bc.edu/RePEc/bocode/b@ 'BENFORD': module to test Benford's Law on a variable / benford reads in the values of a variable and returns a 4 column / table: Column 1 contains the digits 1 through 9, column 2 / contains the total number of values in the variable starting / with that digit, Column 3 contains the percentage @net:describe blp, from(http://fmwww.bc.edu/RePEc/bocode/b)!blp from http://fmwww.bc.edu/RePEc/bocode/b@ 'BLP': module to estimate Berry Levinsohn Pakes random coefficients logit estimator / blp estimates the random parameters logit demand model from / product market shares, using the algorithm proposed by Berry / Levinsohn and Pakes(1995). This allows for endogenous prices, / and individual @net:describe bmtest, from(http://fmwww.bc.edu/RePEc/bocode/b)!bmtest from http://fmwww.bc.edu/RePEc/bocode/b@ 'BMTEST': module for computing the independent two-sample Brunner-Munzel test / bmtest implements the Brunner–Munzel test for two independent / samples (Brunner & Munzel 2000; Neubert & Brunner 2007; Karch / 2023) featuring fewer assumptions than the / Wilcoxon–Mann–Whitney test @net:describe boost, from(http://fmwww.bc.edu/RePEc/bocode/b)!boost from http://fmwww.bc.edu/RePEc/bocode/b@ 'BOOST': module to perform boosted regression / boost implements the MART boosting algorithm described in / Hastie et al. (2001). boost accommodates Gaussian (normal), / logistic, Poisson and multinomial regression. The algorithm is / implemented as a C++ plugin and requires @net:describe c_ml_stata_cv, from(http://fmwww.bc.edu/RePEc/bocode/c)!c_ml_stata_cv from http://fmwww.bc.edu/RePEc/bocode/c@ 'C_ML_STATA_CV': module to implement machine learning classification in Stata / c_ml_stata is a command for implementing machine learning / classification algorithms in Stata 16. It uses the / Stata/Python integration (sfi) capability of Stata 16 and allows / to implement the @net:describe care, from(http://fmwww.bc.edu/RePEc/bocode/c)!care from http://fmwww.bc.edu/RePEc/bocode/c@ 'CARE': module to randomly generate helpful tips to keep you on top of your mental wellbeing / We can all be a little overwhelmed sometimes. Everyone could do / with a reminder to check-in with themselves, a fun fact or an / idea to unwind through the care command. / KW: mental health @net:describe caterpillar, from(http://fmwww.bc.edu/RePEc/bocode/c)!caterpillar from http://fmwww.bc.edu/RePEc/bocode/c@ 'CATERPILLAR': module to generate confidence intervals, Bonferroni-corrected confidence intervals, and null distribution / caterpillar takes a set of estimates and est standard errors / se, along with a unique identifier id for each estimate. The / estimates may @net:describe cddens, from(http://fmwww.bc.edu/RePEc/bocode/c)!cddens from http://fmwww.bc.edu/RePEc/bocode/c@ 'CDDENS': module to estimate a Density under Measurement Error using Auxiliary Information / cddens consistently estimates the density of a variable that is / only observed with error using parameter estimates of the / conditional density of the true value given the observed value. / It @net:describe cdfquantreg, from(http://fmwww.bc.edu/RePEc/bocode/c)!cdfquantreg from http://fmwww.bc.edu/RePEc/bocode/c@ 'CDFQUANTREG': module for estimating generalized linear models for doubly-bounded random variables with cdf-quantile distributions / cfquantreg estimates generalized linear models with cdf-quantile / distributions for doubly-bounded random variables. It assumes / that the @net:describe cdfquantreg01, from(http://fmwww.bc.edu/RePEc/bocode/c)!cdfquantreg01 from http://fmwww.bc.edu/RePEc/bocode/c@ 'CDFQUANTREG01': module for estimating generalized linear models for doubly-bounded random variables with finite-tailed cdf-quantile distributions / cdfquantreg01 estimates generalized linear models with / finite-tailed cdf-quantile distributions for doubly-bounded / random @net:describe cemimix, from(http://fmwww.bc.edu/RePEc/bocode/c)!cemimix from http://fmwww.bc.edu/RePEc/bocode/c@ 'CEMIMIX': module to perform reference-based multiple imputation of cost-effectiveness data in clinical trials / The cemimix module imputes missing numerical cost and/or / effectiveness variables following a reference-based multiple / imputation approach. It follows the method @net:describe cfitzrw, from(http://fmwww.bc.edu/RePEc/bocode/c)!cfitzrw from http://fmwww.bc.edu/RePEc/bocode/c@ 'CFITZRW': module to implement Christiano-Fitzgerald Random Walk band pass filter for timeseries data / cfitzrw filters one or more time series using the / Christiano-Fitzgerald Random Walk band-pass filter described in / Christiano and Fitzgerald (Int Econ Rev, 2003). They demonstrate / that @net:describe chaidforest, from(http://fmwww.bc.edu/RePEc/bocode/c)!chaidforest from http://fmwww.bc.edu/RePEc/bocode/c@ 'CHAIDFOREST': module to conduct random forest ensemble classification based on chi-square automated interaction detection (CHAID) as base learner / Implements random forest ensemble classifier (Breiman, 2001; / Machine Learning) using the CHAID (Chi-square automated / interaction @net:describe chaos, from(http://fmwww.bc.edu/RePEc/bocode/c)!chaos from http://fmwww.bc.edu/RePEc/bocode/c@ 'CHAOS': module to iterate a logistic difference equation / chaos iterates, for 0 < r < 4 and t = 1, 2, 3, ..., x[t+1] = r * / x[t] * (1 - x[t]), starting with x[1] as a random number from / the uniform distribution on [0,1). chaos presents the user with / graphs of x[t] @net:describe choi_lr_test, from(http://fmwww.bc.edu/RePEc/bocode/c)!choi_lr_test from http://fmwww.bc.edu/RePEc/bocode/c@ 'CHOI_LR_TEST': module to perform Choi's likelihood ratio test / choi_lr_test calculates the likelihood ratio (LR) test described / in Choi et al. (2015). This test is conditioned on the total / number of exposed subjects from a case-control study. The / following statistics, @net:describe clan, from(http://fmwww.bc.edu/RePEc/bocode/c)!clan from http://fmwww.bc.edu/RePEc/bocode/c@ 'CLAN': module to perform cluster-level analysis of cluster randomised trials / The clan command performs analysis of cluster randomised trials / by comparing the cluster-level summary measures (proportions, / means, or rates), following the approach suggested by Hayes and / Moulton. It allows @net:describe cltest, from(http://fmwww.bc.edu/RePEc/bocode/c)!cltest from http://fmwww.bc.edu/RePEc/bocode/c@ 'CLTEST': modules for performing cluster-adjusted chi-square and t-tests / cltest is a pair of programs for analyzing data that have been / randomized in clusters. clttest compares the means of two sets / of grouped outcomes, with optional support for group-stratified / outcomes. @net:describe cluster2, from(http://fmwww.bc.edu/RePEc/bocode/c)!cluster2 from http://fmwww.bc.edu/RePEc/bocode/c@ 'CLUSTER2': module to perform power analysis for two-level cluster randomized trials / Determination of sample size, power, and minimum detectable / effect size for two-level cluster randomized trials with / continuous outcomes, with or without a baseline covariate based / on @net:describe cluster3, from(http://fmwww.bc.edu/RePEc/bocode/c)!cluster3 from http://fmwww.bc.edu/RePEc/bocode/c@ 'CLUSTER3': module to perform power analysis for up to three-level cluster randomized trial / The program cluster3 conducts a generalized power analysis for / up to three-level cluster-randomized trials with one treatment / and one control group based on Heo and Leon (2008). / KW: @net:describe clustersampsim, from(http://fmwww.bc.edu/RePEc/bocode/c)!clustersampsim from http://fmwww.bc.edu/RePEc/bocode/c@ 'CLUSTERSAMPSIM': module to simulate cluster-randomized trial sample size requirements / clustersampsim is an extension of the clustersampsi command that / allows the user to specify either 1) a range of numbers of / clusters per study arm to find the required sample sizes per / cluster 2) @net:describe clustpop, from(http://fmwww.bc.edu/RePEc/bocode/c)!clustpop from http://fmwww.bc.edu/RePEc/bocode/c@ 'CLUSTPOP': module to estimate population cluster group assignments / clustpop is a routine to estimate population group assignments / using the cluster command. The cluster command groups cases / based on the values of a variable, or the mean/median of a group / of variables. However, the @net:describe clustsens, from(http://fmwww.bc.edu/RePEc/bocode/c)!clustsens from http://fmwww.bc.edu/RePEc/bocode/c@ 'CLUSTSENS': module to perform sensitivity analysis for cluster commands / clustsens is a routine to do a sensitivity analysis of the / cluster command. The cluster command groups cases based on the / values of a variable, or the mean/median of a group of / variables. The results vary @net:describe cmp, from(http://fmwww.bc.edu/RePEc/bocode/c)!cmp from http://fmwww.bc.edu/RePEc/bocode/c@ 'CMP': module to implement conditional (recursive) mixed process estimator / cmp estimates multi-equation, mixed process models, potentially / with hierarchical random effects. "Mixed process" means that / different equations can have different kinds of dependent / variables. The choices @net:describe conjoint, from(http://fmwww.bc.edu/RePEc/bocode/c)!conjoint from http://fmwww.bc.edu/RePEc/bocode/c@ 'CONJOINT': module to analyse and visualise conjoint (factorial) experiments / conjoint can analyse and visualise conjoint (factorial) / experiments. More specifically, conjoint can estimate average / marginal component effects (AMCE) and marginal means (MM) / following the methods @net:describe crossfold, from(http://fmwww.bc.edu/RePEc/bocode/c)!crossfold from http://fmwww.bc.edu/RePEc/bocode/c@ 'CROSSFOLD': module to perform k-fold cross-validation / crossfold performs k-fold cross-validation on a specified model / in order to evaluate a model's ability to fit out-of-sample / data. This procedure splits the data randomly into k partitions, / then for each partition it @net:describe cvcrand, from(http://fmwww.bc.edu/RePEc/bocode/c)!cvcrand from http://fmwww.bc.edu/RePEc/bocode/c@ 'CVCRAND': module for efficient design and analysis of Cluster Randomized Trials / Cluster randomized trials (CRTs), where clusters (for example, / schools or clinics) are randomized but measurements are taken on / individuals, are commonly used to evaluate interventions in / public @net:describe cwmglm, from(http://fmwww.bc.edu/RePEc/bocode/c)!cwmglm from http://fmwww.bc.edu/RePEc/bocode/c@ 'CWMGLM': module to estimate Cluster Weighted Models (CWM) / cwmglm is a flexible package that allows to estimate Cluster / Weighted Models (finite mixture of regression with random / covariates) using the EM algorithm. In this program, are also / included parsimonious models of Gaussian @net:describe dagumfit, from(http://fmwww.bc.edu/RePEc/bocode/d)!dagumfit from http://fmwww.bc.edu/RePEc/bocode/d@ 'DAGUMFIT': module to fit a Dagum distribution by maximum likelihood / dagumfit fits by ML the 3 parameter Dagum distribution to / sample observations on a random variable. dagumpred / calculates statistics summarizing a Dagum distribution which / has been fitted using dagumfit. @net:describe demotivate, from(http://fmwww.bc.edu/RePEc/bocode/d)!demotivate from http://fmwww.bc.edu/RePEc/bocode/d@ 'DEMOTIVATE': module to remind users of the harsh reality of econometric & statistical practice / demotivate provides quotes for users directly in the results / window as they endlessly toil at the coalface of data analysis. / The random quotes, somewhat humorous, provide a much needed / @net:describe didplacebo, from(http://fmwww.bc.edu/RePEc/bocode/d)!didplacebo from http://fmwww.bc.edu/RePEc/bocode/d@ 'DIDPLACEBO': module for in-time, in-space and mixed placebo tests for estimating difference-in-differences (DID) models / didplacebo implements placebo tests for estimating / difference-in-differences (DID) models, where policy adoption may / be synchronized or staggered. In particular, @net:describe directma, from(http://fmwww.bc.edu/RePEc/bocode/d)!directma from http://fmwww.bc.edu/RePEc/bocode/d@ 'DIRECTMA': module to conducts multiple pair-wise meta-analysis (head-to-head comparisons) and export the pooled results to Excel / directma conducts multiple pair-wise meta-analysis (head-to-head / comparisons) in one single command. All pooled results are / suppressed @net:describe distill, from(http://fmwww.bc.edu/RePEc/bocode/d)!distill from http://fmwww.bc.edu/RePEc/bocode/d@ 'DISTILL': module to assess heterogeneous treatment effects in randomized controlled trials / distill implements the "Distillation Method" (Adams et al. 2022) / to estimate treatment effects within a desired number of strata / (quantiles) of the propensity score to determine whether a / @net:describe dltable, from(http://fmwww.bc.edu/RePEc/bocode/d)!dltable from http://fmwww.bc.edu/RePEc/bocode/d@ 'DLTABLE': module to produce regression tables for Randomized Controlled Trials Using Double LASSO / dltable creates regressions and tables (with the subcommand / using) for experimental studies using double LASSO estimation / (Belloni et al., 2014) It is the sister command of rctable. / @net:describe drmeta, from(http://fmwww.bc.edu/RePEc/bocode/d)!drmeta from http://fmwww.bc.edu/RePEc/bocode/d@ 'DRMETA': module for dose-response meta-analysis / drmeta estimates parametric dose-response models based on / summarized data. It can be used to investigate linear / and non-linear dose-response relationships. It fits fixed-effects / and random-effects model using a one-stage or a / @net:describe eefanalytics, from(http://fmwww.bc.edu/RePEc/bocode/e)!eefanalytics from http://fmwww.bc.edu/RePEc/bocode/e@ 'EEFANALYTICS': module for Evaluating Educational Interventions using Randomised Controlled Trial Designs / Analysing data from evaluations of educational interventions / using a randomised controlled trial design. Various analytical / tools to perform sensitivity analysis using different @net:describe egenmore, from(http://fmwww.bc.edu/RePEc/bocode/e)!egenmore from http://fmwww.bc.edu/RePEc/bocode/e@ 'EGENMORE': modules to extend the generate function / This package includes various -egen- functions. For full / details, please read the help file (ssc type egenmore.sthlp). / Some of these routines are updates of those published in STB-50. / _gfilter, _ggroup2, _gegroup and _gcorr @net:describe estrat, from(http://fmwww.bc.edu/RePEc/bocode/e)!estrat from http://fmwww.bc.edu/RePEc/bocode/e@ 'ESTRAT': module to perform Endogenous Stratification for Randomized Experiments / Estrat uses leave-one-out (LOO) and repeated split sample (RSS) / estimators to obtain estimates of treatment effects for subgroups / in randomized experiments. For further details see Abadie, / Chingos, @net:describe exampleobs, from(http://fmwww.bc.edu/RePEc/bocode/e)!exampleobs from http://fmwww.bc.edu/RePEc/bocode/e@ 'EXAMPLEOBS': module to prints example observations / Prints (randomly selected) example observations and optionally / stores the examples in a local macro / KW: examples / KW: sample observations / KW: data tools / Requires: Stata version 10 / Distribution-Date: 20160205 / Author: Sean @net:describe fastxtile, from(http://fmwww.bc.edu/RePEc/bocode/f)!fastxtile from http://fmwww.bc.edu/RePEc/bocode/f@ 'FASTXTILE': module to generate a variable of quantile categories / fastxtile is a drop in replacement for the built-in Stata / program xtile. It has the same syntax and produces identical / results, but has been optimized to be more computationally / efficient. The difference in @net:describe fiskfit, from(http://fmwww.bc.edu/RePEc/bocode/f)!fiskfit from http://fmwww.bc.edu/RePEc/bocode/f@ 'FISKFIT': module to fit a Fisk distribution by ML to unit record data / fiskfit fits by ML the 2 parameter Fisk (1961) or log-logistic / distribution, optionally as dependent on covariates, to sample / observations on a random variable var. Unit record data are / assumed @net:describe fitstat_ers, from(http://fmwww.bc.edu/RePEc/bocode/f)!fitstat_ers from http://fmwww.bc.edu/RePEc/bocode/f@ 'FITSTAT_ERS': module to compute goodness of fit statistics for Rasch model / fitstat_ers computes outfit and infit statistics for the / conditional maximum likelihood (cml) Rasch model, using formulas / outlined in the Linacre and Wright (1994). These fit statistics / differ @net:describe fragility, from(http://fmwww.bc.edu/RePEc/bocode/f)!fragility from http://fmwww.bc.edu/RePEc/bocode/f@ 'FRAGILITY': module to compute the fragility index and quotient / fragility computes both the fragility index as described in / Walsh et al. (2014) and the fragility quotient as proposed by / Ahmed et al. (2016). The fragility index represents the absolute / number of additional events @net:describe frontierhtail, from(http://fmwww.bc.edu/RePEc/bocode/f)!frontierhtail from http://fmwww.bc.edu/RePEc/bocode/f@ 'FRONTIERHTAIL': module to estimate stochastic production frontier models for heavy tail data / frontierhtail implements stochastic production frontier / regression for heavy tail data. As pointed out by Nguyen (2010), / economic and financial data frequently evidence fat tails. / @net:describe gb2fit, from(http://fmwww.bc.edu/RePEc/bocode/g)!gb2fit from http://fmwww.bc.edu/RePEc/bocode/g@ 'GB2FIT': module to fit Generalized Beta of the Second Kind distribution by maximum likelihood / gb2fit fits by ML the 4 parameter Generalized Beta / distribution of the second kind (GB2) to sample observations / on a random variable. gb2pred calculates statistics summarizing / a GB2 @net:describe gb2lfit, from(http://fmwww.bc.edu/RePEc/bocode/g)!gb2lfit from http://fmwww.bc.edu/RePEc/bocode/g@ 'GB2LFIT': module to fit Generalized Beta of the Second Kind distribution by maximum likelihood (log parameter metric) / gb2lfit fits by ML the 4 parameter Generalized Beta / distribution of the second kind (GB2) to sample observations / on a random variable. gb2pred calculates statistics @net:describe gentrun, from(http://fmwww.bc.edu/RePEc/bocode/g)!gentrun from http://fmwww.bc.edu/RePEc/bocode/g@ 'GENTRUN': module to generate truncated normal variate / gentrun generates random draws from a truncated standard normal / distribution. It allows one-sided and two-sided truncations of / the distribution. Random draws from a non-truncated standard / normal distribution are also permissible. @net:describe getfilename2, from(http://fmwww.bc.edu/RePEc/bocode/g)!getfilename2 from http://fmwww.bc.edu/RePEc/bocode/g@ 'GETFILENAME2': program for handling filename specifications / getfilename2 returns in macro variables the filename, the path, / the root, and the extension of a given filename. / KW: panel data / KW: random effects / KW: weights / Requires: Stata version 8 / @net:describe getmstatistic, from(http://fmwww.bc.edu/RePEc/bocode/g)!getmstatistic from http://fmwww.bc.edu/RePEc/bocode/g@ 'GETMSTATISTIC': module to Quantify Systematic Heterogeneity in Meta-Analysis / getmstatistic computes M statistics to assess the contribution / of each participating study in a meta-analysis. Its primary use / is to identify outlier studies, which either show "null" effects / @net:describe getprime, from(http://fmwww.bc.edu/RePEc/bocode/g)!getprime from http://fmwww.bc.edu/RePEc/bocode/g@ 'GETPRIME': module to get the prime number closer to the specified number / getprime gets the prime number that is closer to number. This / becomes useful, for example, when wanting to have about a certain / number of draws for simulation. In particular for Hemmersley / and Halton sequences @net:describe ginireg, from(http://fmwww.bc.edu/RePEc/bocode/g)!ginireg from http://fmwww.bc.edu/RePEc/bocode/g@ 'GINIREG': module for Gini regression / The ginireg package supports the estimation of Gini regressions. / The Gini regression has its origin in Corrado Gini's (1912) / introduction of the Gini Mean Difference (GMD) as an alternative / to the variance. The population GMD is defined as GMD = / @net:describe gllamm, from(http://fmwww.bc.edu/RePEc/bocode/g)!gllamm from http://fmwww.bc.edu/RePEc/bocode/g@ 'GLLAMM': program to fit generalised linear latent and mixed models / gllamm fits generalized linear latent and mixed models. These / models include Multilevel generalized linear regression models / (extensions of the simple random intercept models that may be / fitted in Stata using @net:describe glst, from(http://fmwww.bc.edu/RePEc/bocode/g)!glst from http://fmwww.bc.edu/RePEc/bocode/g@ 'GLST': module for trend estimation of summarized dose-response data / glst estimates trend across different exposure levels for either / single or multiple summarized case-control, incidence-rate, and / cumulative incidence data. This approach is based on / constructing an approximate @net:describe gmlabvpos, from(http://fmwww.bc.edu/RePEc/bocode/g)!gmlabvpos from http://fmwww.bc.edu/RePEc/bocode/g@ 'GMLABVPOS': module providing egen function for label positioning / _gmlabvpos is an attempt to automatically generate a variable for / the clockpositions of marker labels in scatterplots. That is, the / command generates a variable which can be filled into the scatter / option @net:describe gmmcovearn, from(http://fmwww.bc.edu/RePEc/bocode/g)!gmmcovearn from http://fmwww.bc.edu/RePEc/bocode/g@ 'GMMCOVEARN': module to compute GMM estimates of the Covariance Structure of Earnings / This command estimates the covariance structure of earnings for a / variety of models using the GMM estimator. The program estimates / models that incorporate time factor loadings and cohort factor / loadings @net:describe gridsearch, from(http://fmwww.bc.edu/RePEc/bocode/g)!gridsearch from http://fmwww.bc.edu/RePEc/bocode/g@ 'GRIDSEARCH': module to optimize tuning parameter levels with a grid search / gridsearch runs a user-specified statistical learning (aka / machine learning) algorithm repeatedly with a grid of values / corresponding to one or two tuning parameters. This facilities / the @net:describe gs2slsxt, from(http://fmwww.bc.edu/RePEc/bocode/g)!gs2slsxt from http://fmwww.bc.edu/RePEc/bocode/g@ 'GS2SLSXT': module to estimate Generalized Spatial Panel Autoregressive Two-Stage Least Squares Regression / gs2slsxt estimates Generalized Spatial Panel Autoregressive / Two-Stage Least Squares Regression / KW: spatial / KW: panel / KW: regression / KW: Between Effects / KW: @net:describe gsample, from(http://fmwww.bc.edu/RePEc/bocode/g)!gsample from http://fmwww.bc.edu/RePEc/bocode/g@ 'GSAMPLE': module to draw a random sample / gsample draws a random sample from the data in memory. Simple / random sampling (SRS) is supported, as well as unequal / probability sampling (UPS), of which sampling with probabilities / proportional to size (PPS) is a special case. Both @net:describe haiku, from(http://fmwww.bc.edu/RePEc/bocode/h)!haiku from http://fmwww.bc.edu/RePEc/bocode/h@ 'HAIKU': module to randomly produce haikus / "for every mood- a haiku for every Stata task to do- a user / written command" / KW: haiku / KW: zen / Requires: Stata version 10 / Distribution-Date: 20201121 / Author: Prashansa Srivastava, NA / Support: email @net:describe hotdeck, from(http://fmwww.bc.edu/RePEc/bocode/h)!hotdeck from http://fmwww.bc.edu/RePEc/bocode/h@ 'HOTDECK': module to impute missing values using the hotdeck method / hotdeck replaces missing values for the variable indicated by its / argument. It should be used within a multiple imputation / sequence since missing values are imputed stochastically rather / than deterministically. @net:describe hotdeckvar, from(http://fmwww.bc.edu/RePEc/bocode/h)!hotdeckvar from http://fmwww.bc.edu/RePEc/bocode/h@ 'HOTDECKVAR': module for hotdeck imputation / The algorithm identifies all donor observations that have no / missing values for any of the variables specified. Missing / values from the same observation are replaced with values from / the same donor observation to preserve correlations. Donor / @net:describe icc23, from(http://fmwww.bc.edu/RePEc/bocode/i)!icc23 from http://fmwww.bc.edu/RePEc/bocode/i@ 'ICC23': module that computes models 2 and 3 of the intra-class correlation / ICC23 computes the intra-class correlation for random effects / models 2 and 3, as described by Shrout and Fleiss, 1979. / (-loneway- computes model 1). The module uses Stata’s repeated / @net:describe iccvar, from(http://fmwww.bc.edu/RePEc/bocode/i)!iccvar from http://fmwww.bc.edu/RePEc/bocode/i@ 'ICCVAR': module to calculate intraclass correlation (ICC) after xtmixed / iccvar is a post-estimation command for xtmixed. After fitting / a 2, 3, or 4 level model with a random intercept (random / slopes are not supported), iccvar will calculate the / intraclass correlation (ICC) @net:describe imputerasch, from(http://fmwww.bc.edu/RePEc/bocode/i)!imputerasch from http://fmwww.bc.edu/RePEc/bocode/i@ 'IMPUTERASCH': module to impute binary data by a Rasch model / imputerasch imputes missing binary data by a Rasch model. The / parameters of the Rasch model are estimated on complete data, / then the missing data are imputed from the estimated / probability for each individual to response @net:describe intcens, from(http://fmwww.bc.edu/RePEc/bocode/i)!intcens from http://fmwww.bc.edu/RePEc/bocode/i@ 'INTCENS': module to perform interval-censored survival analysis / This program fits various distributions by maximum likelihood to / non-negative data which can be left-, right- or interval-censored / or point data. The supported distributions are exponential, / Weibull, Gompertz, @net:describe ipdmetan, from(http://fmwww.bc.edu/RePEc/bocode/i)!ipdmetan from http://fmwww.bc.edu/RePEc/bocode/i@ 'IPDMETAN': module for performing two-stage IPD meta-analysis / A set of routines for conducting two-stage individual / participant meta-analysis, and forest plots for trial subgroup / analysis. The two-stage routine, ‘ipdmetan’, loops over a / series of categories, fits the desired @net:describe irrepro, from(http://fmwww.bc.edu/RePEc/bocode/i)!irrepro from http://fmwww.bc.edu/RePEc/bocode/i@ 'IRREPRO': module to produce a simulation of irreproducible results / Imagine an urn containing w white and b black balls, together / with inexhaustible supplies of white and black balls. Pick one / ball from the urn randomly, and replace it in the urn, together / with a additional @net:describe itsamatch, from(http://fmwww.bc.edu/RePEc/bocode/i)!itsamatch from http://fmwww.bc.edu/RePEc/bocode/i@ 'ITSAMATCH': module to perform matching in multiple group interrupted time-series analysis / itsamatch is a data pre-processing procedure that identifies / units not-exposed to the intervention that will best serve as / matched controls for the single treatment unit, in multiple group / @net:describe itsarand, from(http://fmwww.bc.edu/RePEc/bocode/i)!itsarand from http://fmwww.bc.edu/RePEc/bocode/i@ 'ITSARAND': module for conducting randomization tests for single-case and multiple-baseline AB phase designs / itsarand performs randomization tests on interrupted time series / (ITS) data for a single-case AB design and multiple-baseline AB / phase designs (i.e. multiple single-case AB phase @net:describe ivcrc, from(http://fmwww.bc.edu/RePEc/bocode/i)!ivcrc from http://fmwww.bc.edu/RePEc/bocode/i@ 'IVCRC': module to implement the instrumental variables correlated random coefficients estimator / This Stata module implements the instrumental variables / correlated random coefficients estimator, described in Benson, / Masten, and Torgovitsky (2020), which was proposed in Masten and / @net:describe ivmediate, from(http://fmwww.bc.edu/RePEc/bocode/i)!ivmediate from http://fmwww.bc.edu/RePEc/bocode/i@ 'IVMEDIATE': module to perform Causal mediation analysis in instrumental-variables regressions / ivmediate implements the causal mediation analysis framework for / linear IV models introduced by Dippel et al. (2019). It estimates / three effects: i) the total effect of a @net:describe jnsn, from(http://fmwww.bc.edu/RePEc/bocode/j)!jnsn from http://fmwww.bc.edu/RePEc/bocode/j@ 'JNSN': module to fit Johnson distributions / jnsn comprises four commands that collectively fit parameters of / Johnson distributions by two methods (moment matching and / quantiles), transform a variable into a quasinormal deviate / after fitting Johnson distribution parameter estimates, and / @net:describe kfoldclass, from(http://fmwww.bc.edu/RePEc/bocode/k)!kfoldclass from http://fmwww.bc.edu/RePEc/bocode/k@ 'KFOLDCLASS': module for generating classification statistics of k-fold cross-validation for binary outcomes / kfoldclass performs k-fold cross-validation for regression and / machine learning models with a binary outcome and then produces / classification measures to assist in @net:describe lcmc, from(http://fmwww.bc.edu/RePEc/bocode/l)!lcmc from http://fmwww.bc.edu/RePEc/bocode/l@ 'LCMC': module to estimate latent class missing covariate model for continous main response, ordinal covariate with missing values, and informative selection / lcmc fits a latent class model for a missing ordinal covariate, / mcv, and a continuous main response, y, by Simulated @net:describe ldtest, from(http://fmwww.bc.edu/RePEc/bocode/l)!ldtest from http://fmwww.bc.edu/RePEc/bocode/l@ 'LDTEST': module to compute Lorenz Dominance tests / ldtest computes consistent nonparametric tests of Lorenz / Dominance with individual level data and Independent Random / sampling. ldtestmp computes consistent nonparametric tests of / Lorenz Dominance with individual level data and @net:describe leebounds, from(http://fmwww.bc.edu/RePEc/bocode/l)!leebounds from http://fmwww.bc.edu/RePEc/bocode/l@ 'LEEBOUNDS': module for estimating Lee (2009) treatment effect bounds / leebounds computes treatment effect bounds for samples with / non-random sample selection/attrition as proposed by Lee (Review / of Economic Studies, 2009). The lower and upper bound, / respectively, correspond to @net:describe leedtwoway, from(http://fmwww.bc.edu/RePEc/bocode/l)!leedtwoway from http://fmwww.bc.edu/RePEc/bocode/l@ 'LEEDTWOWAY': module to solve two way labor models / leedtwoway runs two way estimators for labor. The package / provides implementations for a series of estimators for models / with two sided heterogeneity: 1. two way fixed effect estimator / as proposed by Abowd Kramarz and Margolis 2. @net:describe listsome, from(http://fmwww.bc.edu/RePEc/bocode/l)!listsome from http://fmwww.bc.edu/RePEc/bocode/l@ 'LISTSOME': module to list a (possibly random) sample of observations / Like the built-in -list- Stata command, -listsome- lists values / of variables but only for a sample of observations. By default, a / maximum of 20 observations are listed. With the -random- option, / a random sample @net:describe lms, from(http://fmwww.bc.edu/RePEc/bocode/l)!lms from http://fmwww.bc.edu/RePEc/bocode/l@ 'LMS': module to perform least median squares regression fit / lms fits a least median squares regression of varlist on depvar. / Least Median Squares is a robust fitting approach which / attempts to minimize the median squared residual of the / regression (equivalent to minimizing the @net:describe lognfit, from(http://fmwww.bc.edu/RePEc/bocode/l)!lognfit from http://fmwww.bc.edu/RePEc/bocode/l@ 'LOGNFIT': module to fit lognormal distribution by maximum likelihood / lognfit fits by ML the 2 parameter lognormal distribution to / sample observations on a random variable. lognpred calculates / statistics summarizing a lognormal distribution which has been / fitted using @net:describe lomackinlay, from(http://fmwww.bc.edu/RePEc/bocode/l)!lomackinlay from http://fmwww.bc.edu/RePEc/bocode/l@ 'LOMACKINLAY': module to perform Lo-MacKinlay variance ratio test / lomackinlay computes a overlapping variance-ratio test on a / timeseries. The timeseries should be in level form; e.g., to / test that stock returns vary randomly around a constant mean, / you consider the null hypothesis @net:describe lowyseattleb, from(http://fmwww.bc.edu/RePEc/bocode/l)!lowyseattleb from http://fmwww.bc.edu/RePEc/bocode/l@ 'LOWYSEATTLEB': module for creating, transforming, labeling, variables / lowyseattleb contains modules that are generally shortcuts for / generating/transforming data, including: collapsing, generating / random ids, encoding and labeling, regularizing multiple date / formats, dropping @net:describe mahapick, from(http://fmwww.bc.edu/RePEc/bocode/m)!mahapick from http://fmwww.bc.edu/RePEc/bocode/m@ 'MAHAPICK': module to select matching observations based on a Mahalanobis distance measure / mahapick seeks matching "control" observations for a set of / "treated" observations. mahascore and mahascores compute the / distance measures. mahascore2 computes a distance between two / points or @net:describe manski_ci, from(http://fmwww.bc.edu/RePEc/bocode/m)!manski_ci from http://fmwww.bc.edu/RePEc/bocode/m@ 'MANSKI_CI': module to use Manski type bounds (Manski 2003) to calculate confidence intervals around a treatment variable's regression coefficient in a (covariate-adjusted) regression / manski_ci is designed for use in the context of randomized / controlled trials (RCTs) with missing outcomes @net:describe maxwell, from(http://fmwww.bc.edu/RePEc/bocode/m)!maxwell from http://fmwww.bc.edu/RePEc/bocode/m@ 'MAXWELL': module for computing Maxwell's random error (RE) coefficient of agreement between 2 raters for binary data / maxwell computes Maxwell's random error (RE) coefficient of / agreement between 2 raters on a binary variable (Maxwell 1977). / The RE coefficient measures the excess of @net:describe mediation, from(http://fmwww.bc.edu/RePEc/bocode/m)!mediation from http://fmwww.bc.edu/RePEc/bocode/m@ 'MEDIATION': module for causal mediation analysis and sensitivity analysis / mediation estimates the role of particular causal mechanisms / that mediate a relationship between treatment and outcome / variables. Calculates causal mediation effects and direct effects / for models with @net:describe merlin, from(http://fmwww.bc.edu/RePEc/bocode/m)!merlin from http://fmwww.bc.edu/RePEc/bocode/m@ 'MERLIN': module to fit mixed effects regression for linear and non-linear models / merlin fits linear, non-linear and user-defined mixed effects / regression models. merlin can fit multivariate outcome models of / any type, each of which could be repeatedly measured / (longitudinal), with @net:describe meta_analysis, from(http://fmwww.bc.edu/RePEc/bocode/m)!meta_analysis from http://fmwww.bc.edu/RePEc/bocode/m@ 'META_ANALYSIS': module to perform subgroup and regression-type fixed- and random-effects meta-analyses / The meta_analysis module includes four commands: masum, maanova, / mareg, and maforest. The first three perform an overall / meta-analysis, a subgroup or categorical moderator analysis, @net:describe metaan, from(http://fmwww.bc.edu/RePEc/bocode/m)!metaan from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAAN': module to perform fixed- or random-effects meta-analyses / The metaan command performs a meta-analysis on a set of studies / and calculates the overall effect and a confidence interval for / the effect. The command also displays various heterogeneity / measures: Cochrane's Q, @net:describe metacum, from(http://fmwww.bc.edu/RePEc/bocode/m)!metacum from http://fmwww.bc.edu/RePEc/bocode/m@ 'METACUM': module to perform cumulative meta-analysis, with graphics / metacum provides cumulative pooled estimates and confidence / limits obtained from fixed or random effects meta-analysis and / plots the cumulative pooled estimates in the style of Lau et al. / (1992). / KW: @net:describe metadta, from(http://fmwww.bc.edu/RePEc/bocode/m)!metadta from http://fmwww.bc.edu/RePEc/bocode/m@ 'METADTA': module to perform fixed- and random-effects meta-analysis and meta-regression of diagnostic accuracy studies / metadta is a routine that performs meta-analytical pooling of / diagnostic accuracy data from separate studies with similar / methodology and epidemiology. The routine @net:describe metafrag, from(http://fmwww.bc.edu/RePEc/bocode/m)!metafrag from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAFRAG': module to compute the fragility index for meta-analysis / metafrag is an application of the fragility index for single / studies with a binary outcome (Walsh et al. 2014) to / meta-analysis (Atal et al. 2019). The fragility index for / meta-analysis is defined as the minimum @net:describe metagen, from(http://fmwww.bc.edu/RePEc/bocode/m)!metagen from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAGEN': module to perform meta-analysis of genetic-association studies / metagen performs fixed-, and random-effects meta-analysis of / genetic association case-control studies using Individual Patient / Data (IPD). metagen performs meta-analysis using fixed- and / random-effects logistic @net:describe metamiss2, from(http://fmwww.bc.edu/RePEc/bocode/m)!metamiss2 from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAMISS2': module accounting for missing outcome data in meta-analysis / Missing outcome data are common in randomized controlled / trials. If they are ignored then the estimated treatment effects / might be biased. Meta-analysts usually assume that the / missing data problem has been @net:describe metan, from(http://fmwww.bc.edu/RePEc/bocode/m)!metan from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAN': module for fixed and random effects meta-analysis / Meta-analysis is a statistical technique for combining results / from multiple independent studies, with the aim of estimating a / single overall effect. The routines in this package provide / facilities to conduct meta-analyses @net:describe metapow, from(http://fmwww.bc.edu/RePEc/bocode/m)!metapow from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAPOW': module for simulation based sample size calculations for designing new clinical trials and diagnostic test accuracy studies to update an existing meta-analysis / The metapow package contains a suite of programs which enable / the user to estimate the probability the @net:describe metapred, from(http://fmwww.bc.edu/RePEc/bocode/m)!metapred from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAPRED': module producing outlier and influence diagnostics for meta-analysis / metapred extends the currently available post-estimation / predictions for meta regress to include standardized residuals, / studentized residuals, DFITS, Cook's distance, Welsch distance, / and @net:describe metapreg, from(http://fmwww.bc.edu/RePEc/bocode/m)!metapreg from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAPREG': module to compute fixed and random effects meta-analysis and meta-regression of proportions / This routine provides procedures for pooling proportions in a / meta-analysis of multiple studies study and/or displays the / results in a forest plot. The pooled estimates are a @net:describe metaprop, from(http://fmwww.bc.edu/RePEc/bocode/m)!metaprop from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAPROP': module to perform fixed and random effects meta-analysis of proportions / This routine provides procedures for pooling proportions in a / meta-analysis of multiple studies study and/or displays the / results in a forest plot. The confidence intervals are based on / score(Wilson) @net:describe metaprop_one, from(http://fmwww.bc.edu/RePEc/bocode/m)!metaprop_one from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAPROP_ONE': module to perform fixed and random effects meta-analysis of proportions / This routine provides procedures for pooling proportions in a / meta-analysis of multiple studies study and/or displays the / results in a forest plot. The pooled estimate is obtained as a / weighted @net:describe metareg, from(http://fmwww.bc.edu/RePEc/bocode/m)!metareg from http://fmwww.bc.edu/RePEc/bocode/m@ 'METAREG': module to perform meta-analysis regression / metareg performs random-effects meta-regression on study-level / summary data. This is a revised version of the program / originally written by Stephen Sharp (STB-42, sbe23). The major / revisions involve improvements to the @net:describe metastrong, from(http://fmwww.bc.edu/RePEc/bocode/m)!metastrong from http://fmwww.bc.edu/RePEc/bocode/m@ 'METASTRONG': module for estimating the proportion of true effect sizes above or below a threshold in random-effects meta-analysis / metastrong estimates evidence strength for scientifically / meaningful effects in meta-analyses under effect heterogeneity / (ie, a nonzero estimated @net:describe metatrend, from(http://fmwww.bc.edu/RePEc/bocode/m)!metatrend from http://fmwww.bc.edu/RePEc/bocode/m@ 'METATREND': module to implement regression methods for detecting trends in cumulative meta-analysis / metatrend performs a cumulative meta-analysis (Lau et al, 1995) / using the DerSimonian and Laird random-effects method and / afterwards, performs two tests for assesing @net:describe mhtexp, from(http://fmwww.bc.edu/RePEc/bocode/m)!mhtexp from http://fmwww.bc.edu/RePEc/bocode/m@ 'MHTEXP': module to perform multiple hypothesis testing correction procedure / mhtexp can be used to perform the MHT correction procedure / explained in List, Shaikh, Xu (2016) - / https://ideas.repec.org/p/feb/natura/00402.html / KW: experiments / KW: @net:describe mi_twoway, from(http://fmwww.bc.edu/RePEc/bocode/m)!mi_twoway from http://fmwww.bc.edu/RePEc/bocode/m@ 'MI_TWOWAY': module for computing scores on questionnaires containing missing item responses / mi_twoway is an implementation of the multiple imputation / procedure proposed by Van Ginkel for computing scores on / questionnaires containing missing item responses. Two / methods are @net:describe mibmi, from(http://fmwww.bc.edu/RePEc/bocode/m)!mibmi from http://fmwww.bc.edu/RePEc/bocode/m@ 'MIBMI': module for cleaning and multiple imputation algorithm for body mass index (BMI) in longitudinal datasets / mibmi is a multiple imputation and cleaning command for body / mass index (BMI), compatible with {cmd:mi} commands. Cleaning / includes standard cleaning that limits values to a @net:describe mimix, from(http://fmwww.bc.edu/RePEc/bocode/m)!mimix from http://fmwww.bc.edu/RePEc/bocode/m@ 'MIMIX': module to perform reference based multiple imputation for sensitivity analysis of longitudinal clinical trials with protocol deviation / mimix imputes missing numerical outcomes for a longitudinal / trial with protocol deviation under distinct reference group / (typically @net:describe mixmixlogit, from(http://fmwww.bc.edu/RePEc/bocode/m)!mixmixlogit from http://fmwww.bc.edu/RePEc/bocode/m@ 'MIXMIXLOGIT': module to estimate mixed-mixed multinomial logit model / mixmixlogit is a Stata command that implements the mixed-mixed / multinomial logit model (MM-MNL) for binary dependent variable / data. It was first proposed in Keane and Wasi (2013) and Greene / and Hensher @net:describe mixrandregret, from(http://fmwww.bc.edu/RePEc/bocode/m)!mixrandregret from http://fmwww.bc.edu/RePEc/bocode/m@ 'MIXRANDREGRET': module for fitting mixed random regret minimization models / mixrandregret utilizes the mixed random regret minimization / model described in Hensher et al. (2016), which is a mixed / version of the classic random regret minimization model / introduced in Chorus. C. @net:describe mkbilogn, from(http://fmwww.bc.edu/RePEc/bocode/m)!mkbilogn from http://fmwww.bc.edu/RePEc/bocode/m@ 'MKBILOGN': module to create bivariate lognormal variables / mkbilogn creates random variables, var1 and var2, drawn from a / bivariate lognormal distribution defined as follows. As n --> oo, / X1 (var1) and X2 (var2) are such that x1=log(X1) and x2=log(X2) / are bivariate Normal @net:describe mlmr2, from(http://fmwww.bc.edu/RePEc/bocode/m)!mlmr2 from http://fmwww.bc.edu/RePEc/bocode/m@ 'MLMR2': module to compute r-squared measures for models estimated by mixed / mlmr2 produces r-squared measures for models estimated by / mixed. Using the Rights and Sterba (2019; 2021; 2023b) framework / for decomposing the total model-implied outcome variance from a / linear mixed @net:describe mmws, from(http://fmwww.bc.edu/RePEc/bocode/m)!mmws from http://fmwww.bc.edu/RePEc/bocode/m@ 'MMWS': module to perform marginal mean weighting through stratification / mmws implements a method that combines elements of two / propensity score-based techniques, stratification and weighting. / mmws is a data pre-processing procedure that reweights a dataset / to balance the @net:describe moremata, from(http://fmwww.bc.edu/RePEc/bocode/m)!moremata from http://fmwww.bc.edu/RePEc/bocode/m@ 'MOREMATA': module (Mata) to provide various functions / This package includes various Mata functions. kern(): various / kernel functions; kint(): kernel integral functions; kdel0(): / canonical bandwidth of kernel; quantile(): quantile function; / median(): median; iqrange(): @net:describe motivate, from(http://fmwww.bc.edu/RePEc/bocode/m)!motivate from http://fmwww.bc.edu/RePEc/bocode/m@ 'MOTIVATE': module providing motivation to users / motivate provides motivational quotes for users directly in the / results window as they continue their work. The randomly / generated quotes are inspirational and/or humorous. Some quotes / are generic while others are @net:describe mundlak, from(http://fmwww.bc.edu/RePEc/bocode/m)!mundlak from http://fmwww.bc.edu/RePEc/bocode/m@ 'MUNDLAK': module to estimate random-effects regressions adding group-means of independent variables to the model / The command mundlak estimates random-effects regression models / (xtreg, re) adding group-means of variables in indepvars which / vary within groups. This @net:describe mvmeta, from(http://fmwww.bc.edu/RePEc/bocode/m)!mvmeta from http://fmwww.bc.edu/RePEc/bocode/m@ 'MVMETA': module to perform multivariate random-effects meta-analysis / mvmeta performs multivariate random-effects meta-analysis and / multivariate random-effects meta-regression on a data-set of / point estimates, variances and (optionally) covariances. It is / an essential @net:describe mvport, from(http://fmwww.bc.edu/RePEc/bocode/m)!mvport from http://fmwww.bc.edu/RePEc/bocode/m@ 'MVPORT': module for Collection, Optimization and Backtest of Financial Portfolios / This package is a set of 12 commands designed to a) collect / online financial data, b) optimize portfolios, c) simulate / portfolios, and d) backtest portfolios. The 12 commands are the / following: 1) @net:describe mvtnorm, from(http://fmwww.bc.edu/RePEc/bocode/m)!mvtnorm from http://fmwww.bc.edu/RePEc/bocode/m@ 'MVTNORM': module to work with the multivariate normal and multivariate t distributions, with and without variable truncation / A set of commands that allows users to evaluate different / distributional quantities of the multivariate normal / distribution, and a particular type of non-central @net:describe nharvey, from(http://fmwww.bc.edu/RePEc/bocode/n)!nharvey from http://fmwww.bc.edu/RePEc/bocode/n@ 'NHARVEY': module to perform Nyblom-Harvey panel test of common stochastic trends / nharvey estimates one form of the test of common stochastic / trends developed by Nyblom and Harvey (NH, 2000). The test is of / the validity of a specified value of the rank of the covariance / matrix of @net:describe nonparmde, from(http://fmwww.bc.edu/RePEc/bocode/n)!nonparmde from http://fmwww.bc.edu/RePEc/bocode/n@ 'NONPARMDE': module to calculate the minimum detectable effect in randomized experiment / nonparmde is a method for calculating the minimum detectable / effect (MDE) using the nonparametric estimators proposed in / Middleton & Aronow (2011). This program is for use on / cluster-level data @net:describe nruns, from(http://fmwww.bc.edu/RePEc/bocode/n)!nruns from http://fmwww.bc.edu/RePEc/bocode/n@ 'NRUNS': module to compute number of runs compared with random shuffles / nruns and nrunsi count runs in a sequence of values showing the / successive occurrence of categories, nruns for a given varname / and nrunsi for values specified in the command line. Each / command shuffles @net:describe nstagebin, from(http://fmwww.bc.edu/RePEc/bocode/n)!nstagebin from http://fmwww.bc.edu/RePEc/bocode/n@ 'NSTAGEBIN': module to provide facilities for optimising and designing multi-arm multi-stage (MAMS) randomised controlled trials with binary outcomes / nstagebin specifies the design (sample size, duration and / overall pairwise operating characteristics) of a multi-arm, / multi-stage @net:describe nstagebinopt, from(http://fmwww.bc.edu/RePEc/bocode/n)!nstagebinopt from http://fmwww.bc.edu/RePEc/bocode/n@ 'NSTAGEBINOPT': module to compute admissible multi-arm multi-stage trial designs with binary outcomes / nstagebinopt searches for multi-arm multi-stage designs with / binary intermediate (I) and definitive (D) outcomes which have / the desired overall type I error rate and power @net:describe nwreciprocity, from(http://fmwww.bc.edu/RePEc/bocode/n)!nwreciprocity from http://fmwww.bc.edu/RePEc/bocode/n@ 'NWRECIPROCITY': module to calculate reciprocity metrics for (weighted) directed networks / nwreciprocity returns the reciprocity coefficient of a given / network, comparing the share of reciprocated ties of the / empirical network to the average of random with same dimensions / (number @net:describe orth_out, from(http://fmwww.bc.edu/RePEc/bocode/o)!orth_out from http://fmwww.bc.edu/RePEc/bocode/o@ 'ORTH_OUT': module to automate and export summary stats/orthogonality tables / orth_out produces summary stats tables and orthogonality tables. / orth_out is useful in testing balance of means across variables / in multiple groups, particularly the balance of baseline / characteristics @net:describe ovbd, from(http://fmwww.bc.edu/RePEc/bocode/o)!ovbd from http://fmwww.bc.edu/RePEc/bocode/o@ 'OVBD': module to generate correlated random binomial data / ovbd generates correlated random binomial data, which might be / useful in Monte Carlo simulations and for similar purposes. / KW: correlated binomial / KW: random variate / KW: overdispersion / KW: underdispersion / @net:describe overid, from(http://fmwww.bc.edu/RePEc/bocode/o)!overid from http://fmwww.bc.edu/RePEc/bocode/o@ 'OVERID': module to conduct postestimation tests of overidentification / overid computes tests of overidentifying restrictions for a / regression estimated via instrumental variables in which the / number of instruments exceeds the number of regressors: that is, / for an overidentified @net:describe paran, from(http://fmwww.bc.edu/RePEc/bocode/p)!paran from http://fmwww.bc.edu/RePEc/bocode/p@ 'PARAN': module to compute Horn's test of principal components/factors / paran is an implementation of Horn's technique for evaluating the / components or factors retained in a principal components analysis / (PCA) or a common factor analysis. According to Horn, a common / interpretation @net:describe paretofit, from(http://fmwww.bc.edu/RePEc/bocode/p)!paretofit from http://fmwww.bc.edu/RePEc/bocode/p@ 'PARETOFIT': module to fit a Type 1 Pareto distribution / paretofit fits by ML a Pareto (Type I) distribution to / sample observations on a random variable. Unit record / KW: Pareto distribution / Requires: Stata version 8.0 / Distribution-Date: 20151111 / Author: Stephen P. Jenkins, London @net:describe parmhet, from(http://fmwww.bc.edu/RePEc/bocode/p)!parmhet from http://fmwww.bc.edu/RePEc/bocode/p@ 'PARMHET': module to produce heterogeneity tests in parmest resultssets / parmhet and parmiv are designed for use with parmest / resultssets, which have one observation per estimated / parameter and data on parameter estimates. parmhet inputs / variables containing parameter estimates, @net:describe pcpanel, from(http://fmwww.bc.edu/RePEc/bocode/p)!pcpanel from http://fmwww.bc.edu/RePEc/bocode/p@ 'PCPANEL': module to perform power calculations for randomized experiments with panel data, allowing for arbitrary serial correlation / This package performs power calculations for randomized / experiments with panel data. Unlike the existing programs / "sampsi" and "power", this package @net:describe pdplot, from(http://fmwww.bc.edu/RePEc/bocode/p)!pdplot from http://fmwww.bc.edu/RePEc/bocode/p@ 'PDPLOT': module to produce Pareto dot plot / pdplot produces a Pareto dot plot as proposed by Wilkinson, / Leland. 2006. Revising the Pareto chart. American Statistician / 60(4): 332-334. The frequencies of a categorical variable are / shown in order by a series of dots against a magnitude @net:describe peerreview, from(http://fmwww.bc.edu/RePEc/bocode/p)!peerreview from http://fmwww.bc.edu/RePEc/bocode/p@ 'PEERREVIEW': module to randomly assign papers to peers for review / peerreview randomly assigns papers to peers for review, based on / the principle of assignment without replacement which / ensures that each paper is assigned an equal number of / times. peerreview can be useful @net:describe perturb, from(http://fmwww.bc.edu/RePEc/bocode/p)!perturb from http://fmwww.bc.edu/RePEc/bocode/p@ 'PERTURB': module to evaluate collinearity and ill-conditioning / perturb is a tool for assessing the impact of small random / changes (perturbations) to variables on parameter estimates. It / is an alternative for collinearity diagnostics such as vif, / collin, coldiag, @net:describe petpoisson, from(http://fmwww.bc.edu/RePEc/bocode/p)!petpoisson from http://fmwww.bc.edu/RePEc/bocode/p@ 'PETPOISSON': module to estimate an Endogenous Participation Endogenous Treatment Poisson model by MSL / petpoisson fits an Endogenous Participation Endogenous / Treatment Poisson model by Maximum Simulated Likelihood. The main / count variable Y is affected by an endogenous @net:describe pinocchio, from(http://fmwww.bc.edu/RePEc/bocode/p)!pinocchio from http://fmwww.bc.edu/RePEc/bocode/p@ 'PINOCCHIO': module that randomly generates false econometric statements / Pinocchio functions as a stochastic producer of spurious / econometric assertions, while the accurate statements are / itemized in the help file. Its purpose is conceived as both a / revision aid and a repository of key @net:describe power_icc, from(http://fmwww.bc.edu/RePEc/bocode/p)!power_icc from http://fmwww.bc.edu/RePEc/bocode/p@ 'POWER_ICC': module to compute power and sample size for one-way random-effects intraclass correlation / power icc computes sample size or power for a one-way / random-effects intraclass correlation, according to Equation 7 of / Zou (2012), which is equivalent to that given by Walter et @net:describe power_swgee, from(http://fmwww.bc.edu/RePEc/bocode/p)!power_swgee from http://fmwww.bc.edu/RePEc/bocode/p@ 'POWER_SWGEE': module to compute power (under both a Z and t distribution) for cluster randomized stepped wedge designs / Stepped wedge cluster randomized trials (SW-CRTs) are / increasingly being used to evaluate interventions in medical and / public health contexts. With the @net:describe ppschromy, from(http://fmwww.bc.edu/RePEc/bocode/p)!ppschromy from http://fmwww.bc.edu/RePEc/bocode/p@ 'PPSCHROMY': module to draw sample with probability proportionate to size, using Chromy's method of sequential random sampling / ppschromy implements Chromy's method of / probability-proportionate-to-size (PPS) sequential random / sampling, including hierarchical serpentine sorting. See Chromy / @net:describe pystacked, from(http://fmwww.bc.edu/RePEc/bocode/p)!pystacked from http://fmwww.bc.edu/RePEc/bocode/p@ 'PYSTACKED': module for stacking generalization and machine learning in Stata / pystacked implements stacked generalization for regression and / binary classification via Python's scikit-learn. Stacking / combines multiple supervised machine learners---the “base” or / “level-0”' @net:describe qcm, from(http://fmwww.bc.edu/RePEc/bocode/q)!qcm from http://fmwww.bc.edu/RePEc/bocode/q@ 'QCM': module to implement quantile control method (QCM) via Random Forest / qcm implements quantile control method (QCM) via random / forest (Chen, Xiao and Yao, 2023), which provides confidence / intervals for treatment effects in panel data with a single / treated unit using quantile random @net:describe qhapipf, from(http://fmwww.bc.edu/RePEc/bocode/q)!qhapipf from http://fmwww.bc.edu/RePEc/bocode/q@ 'QHAPIPF': module to perform analysis of quantitative traits using regression and log-linear modelling when PHASE is unknown / This command models the relationship between a normally / distributed continuous variable in a population-based random / sample and individuals' haplotype. @net:describe quickicc, from(http://fmwww.bc.edu/RePEc/bocode/q)!quickicc from http://fmwww.bc.edu/RePEc/bocode/q@ 'QUICKICC': module to compute intraclass correlation and standard error calculation after xtmixed / quickicc calcuates the intraclass correlation (ICC) after / fitting a two-level xtmixed model where the intercept is the / only random effect. In addition to calculating the ICC, this / program @net:describe r2_mz, from(http://fmwww.bc.edu/RePEc/bocode/r)!r2_mz from http://fmwww.bc.edu/RePEc/bocode/r@ 'R2_MZ': module to compute McKelvey & Zavoina's R2 / r2_mz is a post-estimation command that computes McKelvey & / Zavoina's R2 for multilevel logistic regression, random effects, / and fixed effects logit and probit models. / KW: McKelvey / KW: Zavoina / KW: R2 / KW: @net:describe r_ml_stata_cv, from(http://fmwww.bc.edu/RePEc/bocode/r)!r_ml_stata_cv from http://fmwww.bc.edu/RePEc/bocode/r@ 'R_ML_STATA_CV': module to implement machine learning regression in Stata / r_ml_stata_cv is a command for implementing machine / learning regression algorithms in Stata 16. It uses the / Stata/Python integration (sfi) capability of Stata 16 and allows / to implement the following @net:describe ralloc, from(http://fmwww.bc.edu/RePEc/bocode/r)!ralloc from http://fmwww.bc.edu/RePEc/bocode/r@ 'RALLOC': module to design randomized controlled trials / ralloc provides a sequence of treatments randomly permuted in / blocks of constant or varying size. If not constant, the / size and order of the blocks are also random. Allocation may / be stratified by one or more variables. In @net:describe ralpha, from(http://fmwww.bc.edu/RePEc/bocode/r)!ralpha from http://fmwww.bc.edu/RePEc/bocode/r@ 'RALPHA': module to generate pseudo-random characters or words / ralpha generates a variable with pseudorandom characters/letters / or words. It uses -runiform- to draw random numbers that are / converted or assigned to a list of alpha characters ([A-Za-z]). / The user can tell ralpha to @net:describe randcmd, from(http://fmwww.bc.edu/RePEc/bocode/r)!randcmd from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDCMD': module to compute randomization inference p-values / randcmd computes randomization-c and randomization-t p-values / for individual treatment effects and joint Wald and / Westfall-Young multiple-testing tests of statistical significance / for equations with multiple treatment @net:describe randcmdci, from(http://fmwww.bc.edu/RePEc/bocode/r)!randcmdci from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDCMDCI': module to produce robust randomization-t p-values and confidence intervals for regression coefficients / randcmdci computes randomization confidence intervals and / p-values that are asymptotically robust to deviations from the / sharp null in favour of average treatment @net:describe randcoef, from(http://fmwww.bc.edu/RePEc/bocode/r)!randcoef from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDCOEF': module to estimate correlated random effects and correlated random coefficients models / randcoef estimates both the CRE (Correlated Random Effects) and / the CRC (Correlated Random Coefficients) models allowing / several weighting matrices for the latter. The command uses the @net:describe randinf, from(http://fmwww.bc.edu/RePEc/bocode/r)!randinf from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDINF': module to calculate the treatment effect and p-value of a stratified randomized controlled experiment / randinf is a method for calculating the treatment effect and / p-value of a stratified randomized controlled experiment using / Fisher's Randomization Test under the sharp @net:describe randomid, from(http://fmwww.bc.edu/RePEc/bocode/r)!randomid from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDOMID': module to identify every observation in the dataset with random alphanumeric characters / randomid creates a new variable that uniquely identifies every / observation in the dataset with random alphanumeric characters. / KW: random / KW: data management / Requires: @net:describe randomize, from(http://fmwww.bc.edu/RePEc/bocode/r)!randomize from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDOMIZE': module to create random assignments for experimental trials, including blocking, balance checking, and automated rerandomization / randomize conducts random assignment of units to equally sized / groups. It can check for balance on a specified list of / covariates. If blocking @net:describe randomizr, from(http://fmwww.bc.edu/RePEc/bocode/r)!randomizr from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDOMIZR': module to implement random assignment procedures / randomizr is a Stata translation of the R package randomizr. It / simplifies the design and analysis of randomized experiments. It / covers most experimental designs ranging from block random / assignment to cluster @net:describe randomselect, from(http://fmwww.bc.edu/RePEc/bocode/r)!randomselect from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDOMSELECT': module to randomly select and tag observations / Randomly selects observations and marks them with a dummy / variable. It differs from sample in that it does not drop the / non-selected observations from the data set, and that either / individual observations or other units can @net:describe randomtag, from(http://fmwww.bc.edu/RePEc/bocode/r)!randomtag from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDOMTAG': module to draw observations without replacement / Like Stata's -sample- command, -randomtag- draws observations / without replacement. -randomtag- does not discard observations / but creates instead an indicator variable that tags observations / that are part of @net:describe randtreat, from(http://fmwww.bc.edu/RePEc/bocode/r)!randtreat from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDTREAT': module to randomly assign treatments uneven treatments and deal with misfits / The randtreat command performs random treatment assignment. / It can handle an arbitrary number of treatments and uneven / treatment fractions, which are common in real-world randomized / control @net:describe randtreatseq, from(http://fmwww.bc.edu/RePEc/bocode/r)!randtreatseq from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANDTREATSEQ': module for generating treatments in a random sequence for each individual in the sample / randtreatseq generates treatments in a random sequence for each / individual in the sample, thereby reducing the potential for / order effects of multiple treatments. / KW: Multiple @net:describe ranova, from(http://fmwww.bc.edu/RePEc/bocode/r)!ranova from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANOVA': module to estimate single factor repeated measures ANOVA / The statistical design permits analysis of repeated (treatment) / measures on the same individuals. Total model variability is / divided into: SS Treatment - the variability resulting from the / independent variable; @net:describe ranvar, from(http://fmwww.bc.edu/RePEc/bocode/r)!ranvar from http://fmwww.bc.edu/RePEc/bocode/r@ 'RANVAR': module to compute the random group variance estimator of the mean for survey data / ranvar calculates the random group variance estimator of the / mean for survey data. The estimator is described in detail by / Wolter (1985) "Introduction to Variance estimation", New / @net:describe rassign, from(http://fmwww.bc.edu/RePEc/bocode/r)!rassign from http://fmwww.bc.edu/RePEc/bocode/r@ 'RASSIGN': module to perform regression-based test for random assignment to peer groups / rassign performs a regression-based test for the (conditional) / random assignment of individuals in urns to peer groups / (Jochmans, 2020). The dependent variable is a characteristic of / the @net:describe rcl, from(http://fmwww.bc.edu/RePEc/bocode/r)!rcl from http://fmwww.bc.edu/RePEc/bocode/r@ 'RCL': module for estimation and simulation of random coefficient logit models / rcl estimates and simulates random coefficient logit models / using product level data. The models covered include the random / coefficient logit model of Berry, Levinsohn and Pakes (1995) / (BLP), @net:describe rctable, from(http://fmwww.bc.edu/RePEc/bocode/r)!rctable from http://fmwww.bc.edu/RePEc/bocode/r@ 'RCTABLE': module to create a table used in randomized controlled trials / rctable creates a simple table to be used mainly in Randomized / Controlled Trials or in experimental settings where a treatment / group is compared to a comparison group. rctable creates a table / in your dataset @net:describe rctmiss, from(http://fmwww.bc.edu/RePEc/bocode/r)!rctmiss from http://fmwww.bc.edu/RePEc/bocode/r@ 'RCTMISS': module to analyse a randomised controlled trial (RCT) allowing for informatively missing outcome data / rctmiss analyses a randomised controlled trial with missing / outcome data under a range of assumptions about the missing data. / The data and missingness are modelled jointly @net:describe rdcont, from(http://fmwww.bc.edu/RePEc/bocode/r)!rdcont from http://fmwww.bc.edu/RePEc/bocode/r@ 'RDCONT': module to compute non-randomized approximate sign test of density continuity / Regression discontinuity designs operate under the assumption / that the running variable is continuous at a threshold. rdcont / tests that assumption using a non-randomized approximate sign / @net:describe rdpermute, from(http://fmwww.bc.edu/RePEc/bocode/r)!rdpermute from http://fmwww.bc.edu/RePEc/bocode/r@ 'RDPERMUTE': module to perform a permutation test for the Regression Kink (RK) and Regression Discontinuity (RD) Design / rdpermute implements a permutation test for the Regression Kink / (RK) and Regression Discontinuity (RD) Design for the one / dimensional case of one Outcome @net:describe rdpower, from(http://fmwww.bc.edu/RePEc/bocode/r)!rdpower from http://fmwww.bc.edu/RePEc/bocode/r@ 'RDPOWER': module to perform power calculations for random designs / rdpower computes power for a variety of randomized designs: / a single level randomized design where there is no clustering, / a two-level cluster randomized design where treatment is at / level 2, a three-level @net:describe redi, from(http://fmwww.bc.edu/RePEc/bocode/r)!redi from http://fmwww.bc.edu/RePEc/bocode/r@ 'REDI': module providing a Random Empirical Distribution Imputation method for estimating continuous incomes / redi is a method for cold-deck imputation of a continuous / distribution from binned incomes, using a real-world reference / dataset (in this case, the CPS ASEC). The Random @net:describe reffadjust, from(http://fmwww.bc.edu/RePEc/bocode/r)!reffadjust from http://fmwww.bc.edu/RePEc/bocode/r@ 'REFFADJUST': module to estimate adjusted regression coefficients for the association between two random effects variables / reffadjust provides two postestimation commands, / reffadjustsim and reffadjust4nlcom, to estimate adjusted / regression coefficients for the association between two random @net:describe reggae_music, from(http://fmwww.bc.edu/RePEc/bocode/r)!reggae_music from http://fmwww.bc.edu/RePEc/bocode/r@ 'REGGAE_MUSIC': module for rasta Stata users / reggae_music randomly picks a reggae song to release any coding / stress. / KW: music / KW: reggae / KW: stress reduction / Requires: Stata version 10 / Distribution-Date: 20191014 / Author: Matteo Ruzzante, DIME, World Bank / Support: email @net:describe regoprob, from(http://fmwww.bc.edu/RePEc/bocode/r)!regoprob from http://fmwww.bc.edu/RePEc/bocode/r@ 'REGOPROB': module to estimate random effects generalized ordered probit models / regoprob is a user-written procedure to estimate random effects / generalized ordered probit models in Stata. The actual values / taken on by the dependent variable are irrelevant except that / larger values are @net:describe regoprob2, from(http://fmwww.bc.edu/RePEc/bocode/r)!regoprob2 from http://fmwww.bc.edu/RePEc/bocode/r@ 'REGOPROB2': module to estimate random effects generalized ordered probit models (update) / regoprob2 is a user-written program and an extension of regoprob / (SSC, Stefan Boes) that estimates random effects generalized / ordered probit models for ordinal dependent variables. We added / the @net:describe reporterror, from(http://fmwww.bc.edu/RePEc/bocode/r)!reporterror from http://fmwww.bc.edu/RePEc/bocode/r@ 'REPORTERROR': module to estimate true distribution from noisy measurements / This program executes estimation of the probability masses of / an unobserved discrete random variable using two measurements / with possibly nonclassical and nonseparable measurement errors / based on Hu @net:describe reu, from(http://fmwww.bc.edu/RePEc/bocode/r)!reu from http://fmwww.bc.edu/RePEc/bocode/r@ 'REU': module to compute number of random error units (REU) in epidemiological studies / reu is a post-estimation command that displays the number of / random error units (REU) for continuous and binary predictors / of the previously fitted model (regress, glm, logit, @net:describe rforest, from(http://fmwww.bc.edu/RePEc/bocode/r)!rforest from http://fmwww.bc.edu/RePEc/bocode/r@ 'RFOREST': module to implement Random Forest algorithm / rforest is a plugin for random forest classification and / regression algorithms. It is built on a Java backend which acts / as an interface to the RandomForest Java class presented in / the WEKA project, developed at the University of @net:describe rfregk, from(http://fmwww.bc.edu/RePEc/bocode/r)!rfregk from http://fmwww.bc.edu/RePEc/bocode/r@ 'RFREGK': module to estimate random-effects model with weights / This program estimates a random-effects model with weights. It is / a modification of Stata's xtreg command and accepts aweights / only. Note, the latest robust command for standard errors is not / used. / Author: @net:describe rgb2, from(http://fmwww.bc.edu/RePEc/bocode/r)!rgb2 from http://fmwww.bc.edu/RePEc/bocode/r@ 'RGB2': module to generate GB2 random numbers / rgb2 creates random variates from the Generalized Beta of a / Second Kind Distribution. a, b, p, and q are specified by the / user. Note that all parameter values must be positive. / KW: beta distribution / KW: second kind / KW: random @net:describe rgroup, from(http://fmwww.bc.edu/RePEc/bocode/r)!rgroup from http://fmwww.bc.edu/RePEc/bocode/r@ 'RGROUP': module for Random Group Variance Estimation / rgroup calculates the random group variance estimator described / by Wolter (1985). This estimator is an alternative to other / estimators of the variance of sample statistics for survey data / e.g. the jackknife or taylor @net:describe rhausman, from(http://fmwww.bc.edu/RePEc/bocode/r)!rhausman from http://fmwww.bc.edu/RePEc/bocode/r@ 'RHAUSMAN': module to perform Robust Hausman Specification Test / This command implements a (cluster-)robust version of the / Hausman specification test using a bootstrap procedure. For / example, this test can be used to compare random effects (RE) / vs. fixed effects (FE) models @net:describe rho_xtregar, from(http://fmwww.bc.edu/RePEc/bocode/r)!rho_xtregar from http://fmwww.bc.edu/RePEc/bocode/r@ 'RHO_XTREGAR': module to estimate a consistent and asymptotically unbiased autocorrelation coefficient for xtregar fixed-effects or random-effects linear model with an AR(1) disturbance / rho_xtregar estimates the autoregressive parameter for / cross-sectional time-series regression @net:describe rifle, from(http://fmwww.bc.edu/RePEc/bocode/r)!rifle from http://fmwww.bc.edu/RePEc/bocode/r@ 'RIFLE': module to perform Randomization Inference for Leader Effects / rifle implements randomization inference for leader effects as / developed by Berry and Fowler (2017). It returns a table of / results comparing the R-squared from the true data with the / distribution of R-squareds from @net:describe ritest, from(http://fmwww.bc.edu/RePEc/bocode/r)!ritest from http://fmwww.bc.edu/RePEc/bocode/r@ 'RITEST': module to perform randomization inference and permutation tests / ritest performs randomization inference and permutation tests, / allowing for arbitrary randomization procedures and with (almost) / any Stata command. / KW: randomization inference / KW: permutation tests / @net:describe rnd, from(http://fmwww.bc.edu/RePEc/bocode/r)!rnd from http://fmwww.bc.edu/RePEc/bocode/r@ 'RND': modules for random number generation / The random number generators in this package are updated versions / of those published in STB-28 and STB-44. They allow for / noncentrally distributed random numbers, distributed F, t, or / Chi-squared. The other RNGs include some which are @net:describe rpaxioms, from(http://fmwww.bc.edu/RePEc/bocode/r)!rpaxioms from http://fmwww.bc.edu/RePEc/bocode/r@ 'RPAXIOMS': module to test and evaluate axioms of revealed preferences / The package contains three commands. checkax allows the user to / test whether consumer demand data satisfy certain revealed / preference axioms at a given efficiency level. The command aei / calculates measures of @net:describe rrlogit, from(http://fmwww.bc.edu/RePEc/bocode/r)!rrlogit from http://fmwww.bc.edu/RePEc/bocode/r@ 'RRLOGIT': module to estimate logistic regression for randomized response data / rrlogit fits a maximum-likelihood logistic regression for / randomized response data. / KW: randomized response technique / KW: RRT / KW: logit / Requires: Stata version 9.1 / Distribution-Date: 20110512 / @net:describe rrreg, from(http://fmwww.bc.edu/RePEc/bocode/r)!rrreg from http://fmwww.bc.edu/RePEc/bocode/r@ 'RRREG': module to estimate linear probability model for randomized response data / rrreg fits a linear probability model for randomized response / data. / KW: randomized response / KW: RRT / KW: sensitive questions / KW: linear probability model / Requires: Stata version 9.0 / @net:describe rscore, from(http://fmwww.bc.edu/RePEc/bocode/r)!rscore from http://fmwww.bc.edu/RePEc/bocode/r@ 'RSCORE': module for estimation of responsiveness scores / rscore computes unit-specific responsiveness scores using an / iterated Random-Coefficient-Regression (RCR). The basic / econometrics of this model can be found in Wooldridge (2002, pp. / 638-642). The model @net:describe rsgt, from(http://fmwww.bc.edu/RePEc/bocode/r)!rsgt from http://fmwww.bc.edu/RePEc/bocode/r@ 'RSGT': module to generate skewed generalized t random numbers / rsgt creates random variates from the Skewed Generalized T / Distribution. Mu, Lambda, Sigma, p, and q are specified by the / user. Note that sigma, p, and q are positive and -1 < lambda / <1. / KW: skewed generalized t @net:describe rsort, from(http://fmwww.bc.edu/RePEc/bocode/r)!rsort from http://fmwww.bc.edu/RePEc/bocode/r@ 'RSORT': module to perform reproducible random sorting of dataset / -rsort- randomly sorts the dataset in memory. This is useful for / various situations in which the sort order of the dataset may / affect results, such as sampling without replacement in / propensity score matching. @net:describe rsz, from(http://fmwww.bc.edu/RePEc/bocode/r)!rsz from http://fmwww.bc.edu/RePEc/bocode/r@ 'RSZ': module to draw a stratified simple random sample, a systematic sample, or a randomly split zones sample, with probabilities proportional to size / This program was written primarily for drawing a sample by the / randomly split zones (rsz) for samples of size one sampling / method as @net:describe rtnorm, from(http://fmwww.bc.edu/RePEc/bocode/r)!rtnorm from http://fmwww.bc.edu/RePEc/bocode/r@ 'RTNORM': Mata module to produce truncated normal pseudorandom variates (Mata) / rtnorm() returns truncated normal random variates. The user may / specify both the mean and standard deviation before truncation, / and the interval for the two-side truncation of the distribution. / After @net:describe runmixregls, from(http://fmwww.bc.edu/RePEc/bocode/r)!runmixregls from http://fmwww.bc.edu/RePEc/bocode/r@ 'RUNMIXREGLS': Run the MIXREGLS software from within Stata / / This module runs the MIXREGLS mixed-effects location scale software / (Hedeker and Nordgren 2013) from within Stata. The mixed-effects location / scale model extends the standard two-level random-intercept mixed-effects / model for @net:describe runmlwin, from(http://fmwww.bc.edu/RePEc/bocode/r)!runmlwin from http://fmwww.bc.edu/RePEc/bocode/r@ 'RUNMLWIN': Run the MLwiN multilevel modelling software from within Stata / / This module fits multilevel models in MLwiN from within Stata. / There are three steps to using runmlwin: (1) The researcher specifies / the desired model using the runmlwin command syntax; (2) The model is / sent @net:describe staggered, from(http://fmwww.bc.edu/RePEc/bocode/s)!staggered from http://fmwww.bc.edu/RePEc/bocode/s@ 'STAGGERED': module implementing R staggered package based on Roth and Sant'Anna (2023) / The staggered package computes the efficient estimator for / settings with randomized treatment timing, based on the / theoretical results in Roth and Sant'Anna (2023). If units are / randomly @net:describe stocdom, from(http://fmwww.bc.edu/RePEc/bocode/s)!stocdom from http://fmwww.bc.edu/RePEc/bocode/s@ 'STOCDOM': module to compute bounds assuming stochastic dominance / stocdom calculates bounds for treatment effects in randomized / controlled trials in the presence of survey attrition, when / stochastic dominance is assumed as in Zhang and Rubin (2003). In / this context stochastic @net:describe swpermute, from(http://fmwww.bc.edu/RePEc/bocode/s)!swpermute from http://fmwww.bc.edu/RePEc/bocode/s@ 'SWPERMUTE': module to compute Permutation tests for Stepped-Wedge Cluster-Randomised Trials / Permutation tests are useful in stepped-wedge trials to provide / robust statistical tests of intervention-effect estimates. / However, the Stata command permute does not produce valid @net:describe tmpinv, from(http://fmwww.bc.edu/RePEc/bocode/t)!tmpinv from http://fmwww.bc.edu/RePEc/bocode/t@ 'TMPINV': module to providing a non-iterated Transaction Matrix (TM)-specific implementation of the LPLS estimator / The program implements a non-iterated Transaction Matrix / (TM)-specific LPLS estimator for linear programming with the help / of the Moore-Penrose @net:describe tmpinvi, from(http://fmwww.bc.edu/RePEc/bocode/t)!tmpinvi from http://fmwww.bc.edu/RePEc/bocode/t@ 'TMPINVI': module providing an iterated (multistep) Transaction Matrix (TM)-specific implementation of the LPLS estimator / The program implements an iterated (multistep) Transaction / Matrix (TM)-specific LPLS estimator for linear programming with / the help of the Moore-Penrose @net:describe treatrew, from(http://fmwww.bc.edu/RePEc/bocode/t)!treatrew from http://fmwww.bc.edu/RePEc/bocode/t@ 'TREATREW': module to estimate Average Treatment Effects by reweighting on propensity score / treatrew estimates Average Treatment Effects by reweighting on / propensity score as proposed by Rosenbaum and Rubin (1983) in / their seminal article. Depending on the model specified, / @net:describe truernd, from(http://fmwww.bc.edu/RePEc/bocode/t)!truernd from http://fmwww.bc.edu/RePEc/bocode/t@ 'TRUERND': module to generate true random numbers / truernd provides an interface to the true random number service / provided by the random.org website created by Mads Haahr. This / service samples atmospheric noise via radio tuned to an unused / broadcasting frequency together with a skew @net:describe umeta, from(http://fmwww.bc.edu/RePEc/bocode/u)!umeta from http://fmwww.bc.edu/RePEc/bocode/u@ 'UMETA': module for u-statistic-based univariate and multivariate random-effects meta-analysis / The umeta command performs u-statistics-based random-effects / meta-analysis on a dataset of univariate, bivariate or trivariate / point estimates, sampling variances, @net:describe vanelteren, from(http://fmwww.bc.edu/RePEc/bocode/v)!vanelteren from http://fmwww.bc.edu/RePEc/bocode/v@ 'VANELTEREN': module to perform van Elteren's test (generalized Wilcoxon-Mann-Whitney ranksum test) / This implements van Elteren's test, also known as the generalized / Wilcoxon-Mann-Whitney rank sum test. This is an extension of / that test that allows stratification (blocking) by a @net:describe varsearch, from(http://fmwww.bc.edu/RePEc/bocode/v)!varsearch from http://fmwww.bc.edu/RePEc/bocode/v@ 'VARSEARCH': program to search variable names and labels / varsearch searches variable names or labels for a specified / regular expression. It returns the list of matching variables in / a macro variable. / KW: panel data / KW: random effects / KW: weights / Requires: Stata version 9 @net:describe wald_mse, from(http://fmwww.bc.edu/RePEc/bocode/w)!wald_mse from http://fmwww.bc.edu/RePEc/bocode/w@ 'WALD_MSE': module to calculate the maximum mean square error (MSE) of a point estimator of the mean / wald_mse calculates the maximum MSE of a point-estimator of / the mean of a bounded outcome, from a random sample with / missing data. The MSE equals regret under square loss, so the / @net:describe wclogit, from(http://fmwww.bc.edu/RePEc/bocode/w)!wclogit from http://fmwww.bc.edu/RePEc/bocode/w@ 'WCLOGIT': module to perform conditional logistic regression with within-group varying weights / This command maximises the partial log-likelihood for conditional / logistic regression with weights that can vary within the matched / set defined by the group option. In calculations @net:describe white, from(http://fmwww.bc.edu/RePEc/bocode/w)!white from http://fmwww.bc.edu/RePEc/bocode/w@ 'WHITE': module to perform White's test for heteroscedasticity / htest, szroeter, and white provide tests for the assumption of / the linear regression model that the residuals e are / homoscedastic, i.e., have constant variance. The tests differ / with respect to the specification of @net:describe xsmle, from(http://fmwww.bc.edu/RePEc/bocode/x)!xsmle from http://fmwww.bc.edu/RePEc/bocode/x@ 'XSMLE': module for spatial panel data models estimation / Econometricians have begun to devote more attention to spatial / interactions when carrying out applied econometric studies. We / provide the new Stata command -xsmle-, which fits fixed and / random-effects spatial models for @net:describe xtabond2, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtabond2 from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTABOND2': module to extend xtabond dynamic panel data estimator / xtabond2 can fit two closely related dynamic panel data / models. The first is the Arellano-Bond (1991) estimator, which / is also available with xtabond without the two-step / finite-sample correction described @net:describe xtarsim, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtarsim from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTARSIM': module to perform Monte Carlo analysis for dynamic panel data models / The Monte Carlo strategy by McLeod and Hipel (Water Resources / Research, 1978), originally thought for time series data, has / been adapted to dynamic panel data models by Kiviet (1995). This / procedure is @net:describe xtavplot, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtavplot from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTAVPLOT': module to produce added-variable plots for panel data estimation / xtavplot creates an added-variable plot (a.k.a. / partial-regression leverage plot, partial regression plot, or / adjusted partial residual plot) after xtreg, fe (fixed-effects / estimation), @net:describe xtcsd, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtcsd from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTCSD': module to test for cross-sectional dependence in panel data models / xtcsd tests for cross-sectional dependence in Fixed Effects or / Random Effects models. xtcsd tests the hypothesis of / cross-sectional independence in panel data models with small T / and large N by @net:describe xtdpdbc, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtdpdbc from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTDPDBC': module to perform bias-corrected estimation of linear dynamic panel data models / xtdpdbc implements a bias-corrected method-of-moments estimator / for linear dynamic panel data models with fixed or random / effects. Higher-order autoregressive models and unbalanced panel / data @net:describe xtdpdml, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtdpdml from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTDPDML': module to estimate Dynamic Panel Data Models using Maximum Likelihood / Panel data make it possible both to control for unobserved / confounders and to include lagged, endogenous regressors. Trying / to do both at the same time, however, leads to serious estimation / @net:describe xtdpdqml, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtdpdqml from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTDPDQML': module to perform quasi-maximum likelihood linear dynamic panel data estimation / xtdpdqml implements the unconditional quasi-maximum likelihood / estimators of Bhargava and Sargan (1983) for linear dynamic panel / models with random effects and Hsiao, Pesaran, and @net:describe xtfeis, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtfeis from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTFEIS': module to estimate linear Fixed-Effects model with Individual-specific Slopes (FEIS) / The module provides Stata command xtfeis to estimate linear / Fixed-Effects models with Individual-specific Slopes (FEIS). It / also provides commands to compute two versions of the @net:describe xtgeebcv, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtgeebcv from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTGEEBCV': module to compute bias-corrected (small-sample) standard errors for generalized estimating equations / Cluster randomized trials (CRTs), where clusters (e.g., schools / or clinics) are randomized to comparison arms but measurements / are taken on individuals, are commonly @net:describe xthybrid, from(http://fmwww.bc.edu/RePEc/bocode/x)!xthybrid from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTHYBRID': module to estimate hybrid and correlated random effect (Mundlak) models within the framework of generalized linear mixed models (GLMM) / xthybrid estimates generalized linear mixed models that split / the effects of cluster-varying covariates on the outcome variable / into @net:describe xtmixed_corr, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtmixed_corr from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTMIXED_CORR': module to compute model-implied intracluster correlations after xtmixed / Linear mixed models as fit by xtmixed have complex expressions / for intracluster correlation. Correlation comes from two / sources: (1) the design of the random effects and their assumed / @net:describe xtoos, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtoos from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTOOS': module for evaluating the out-of-sample prediction performance of panel-data models / The package XTOOS includes four new commands that allow to / evaluate the out-of-sample prediction performance of panel-data / models in their time-series and cross-individual dimensions / @net:describe xtoverid, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtoverid from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTOVERID': module to calculate tests of overidentifying restrictions after xtreg, xtivreg, xtivreg2, xthtaylor / xtoverid computes versions of a test of overidentifying / restrictions (orthogonality conditions) for a panel data / estimation. For an instrumental variables estimation, this @net:describe xtpdyn, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtpdyn from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTPDYN': module to estimate dynamic random effects probit model with unobserved heterogeneity / xtpdyn fits dynamic random-effects probit models (meprobit and / xtprobit) with unobserved heterogeneity. It implements Wooldridge / simple solution to the initial condition problem @net:describe xtprobitunbal, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtprobitunbal from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTPROBITUNBAL': module to estimate Dynamic Probit Random Effects Models with Unbalanced Panels / This package contains the xtprobitunbal command that implements / method discussed in Albarran et al. (2019) to estimate dynamic / probit correlated random effects models with unbalanced panels. @net:describe xtregam, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtregam from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTREGAM': module to estimate Amemiya Random-Effects Panel Data: Ridge and Weighted Regression / xtregam estimates Amemiya Random-Effects Panel Data with Ridge / and Weighted Regression and calculate Panel Heteroscedasticity, / Model Selection Diagnostic Criteria, and Marginal @net:describe xtregbn, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtregbn from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTREGBN': module to estimate Balestra-Nerlove Random-Effects Panel Data: Ridge and Weighted Regression / xtregbn estimates Balestra-Nerlove Random-Effects Panel Data / with Ridge and Weighted Regression and calculate Panel / Heteroscedasticity, Model Selection Diagnostic Criteria, @net:describe xtreghet, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtreghet from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTREGHET': module to estimate MLE Random-Effects with Multiplicative Heteroscedasticity Panel Data Regression / xtreghet estimates MLE Random-Effects with Multiplicative / Heteroscedasticity Panel Data Regression and calculate Panel / Heteroscedasticity, Model Selection @net:describe xtregmle, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtregmle from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTREGMLE': module to estimate Trevor Breusch MLE Random-Effects Panel Data: Ridge and Weighted Regression / xtregmle estimates Trevor Breusch MLE Random-Effects Panel Data: / Ridge and Weighted Regression and calculate Panel / Heteroscedasticity, Model Selection Diagnostic Criteria, @net:describe xtregre2, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtregre2 from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTREGRE2': module to estimate random effects model with weights / xtregre2 estimates a random effects model with weights. It is an / update to Kevin McKinney's rfregk. xtregre2 only accepts / aweights and the alternative variance estimators are not / supported. / KW: panel data / @net:describe xtregrem, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtregrem from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTREGREM': module to estimate Fuller-Battese GLS Random-Effects Panel Data: Ridge and Weighted Regression / xtregrem estimates Fuller-Battese GLS Random-Effects Panel Data: / Ridge and Weighted Regression and calculate Panel / Heteroscedasticity, Model Selection Diagnostic Criteria, @net:describe xtregsam, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtregsam from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTREGSAM': module to estimate Swamy-Arora Random-Effects Panel Data: Ridge and Weighted Regression / xtregsam estimates Swamy-Arora Random-Effects Panel Data: Ridge / and Weighted Regression and calculate Panel Heteroscedasticity, / Model Selection Diagnostic Criteria, and @net:describe xtregwhm, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtregwhm from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTREGWHM': module to estimate Wallace-Hussain Random-Effects Panel Data: Ridge and Weighted Regression / xtregwhm estimates Wallace-Hussain Random-Effects Panel Data: / Ridge and Weighted Regression and calculate Panel / Heteroscedasticity, Model Selection Diagnostic Criteria, and / @net:describe xtsur, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtsur from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTSUR': module to estimate seemingly unrelated regression model on unbalanced panel data / xtsur fits a many-equation seemingly-unrelated regression (SUR) / model of the y1 variable on the x1 variables and the y2 variable / on the x1 or x2 variables and etc..., using random effect / @net:describe xtvc, from(http://fmwww.bc.edu/RePEc/bocode/x)!xtvc from http://fmwww.bc.edu/RePEc/bocode/x@ 'XTVC': module to compute confidence intervals for the variance component of random-intercept linear models / xtvc is a post-estimation command that presents confidence / intervals for the variance component of the random effect based / on the inversion of a score-based test(Bottai, Biometrika @net:describe parmhet, from(http://www.rogernewsonresources.org.uk/stata10)!parmhet from http://www.rogernewsonresources.org.uk/stata10@ parmhet: Heterogeneity tests in parmest resultssets / parmhet and parmiv are designed for use with parmest resultssets, / which have one observation per estimated parameter and data on / parameter estimates. parmhet inputs variables containing parameter / estimates, standard errors @net:describe uk2017, from(http://www.rogernewsonresources.org.uk/usergp)!uk2017 from http://www.rogernewsonresources.org.uk/usergp@ uk2017: Ridit splines with applications to propensity weighting / Given a random variable X, the ridit function R_X(.) specifies its / distribution. The SSC package wridit can compute ridits (possibly / weighted) for a variable. A ridit spline in a variable X is a spline / in the ridit @net:describe uk2015, from(http://www.rogernewsonresources.org.uk/usergp)!uk2015 from http://www.rogernewsonresources.org.uk/usergp@ uk2015: Somers' D: A common currency for associations / Somers' D(Y|X) is an asymmetric measure of ordinal association between / two variables Y and X, on a scale from -1 to 1. It is defined as the / difference between the conditional probabilities of concordance and / discordance between two @net:describe exquantile, from(http://fmwww.bc.edu/RePEc/bocode/e)!exquantile from http://fmwww.bc.edu/RePEc/bocode/e@ 'EXQUANTILE': module for estimation and inference for (conditional) extremal quantiles / This program estimates (conditional) extremal quantiles based on / the nearest neighbor method for Hill's estimator of the tail / index. If indepvar is absent in the command line, then the / 26 references found in tables of contents ----------------------------------------- @net:from http://www.homepages.ucl.ac.uk/~ucakjpr/stata/!http://www.homepages.ucl.ac.uk/~ucakjpr/stata/@ Materials by Patrick Royston / (Some of these programs are the work of several people.) / These are the {cmd:latest versions} of my software. Some may be less well tested, and some may even / have bugs. If you have problems with a program, please contact me at j.royston@ucl.ac.uk. / @net:from http://www.stata.com/users/wgould/!http://www.stata.com/users/wgould/@ Materials by Bill Gould, StataCorp / Materials created by Bill Gould while working at StataCorp / automatic updating of ado-files work in progress / Mata talk given at Stata user group meetings in 2005 / Mata talk for the 2005 NASUG / Calculate area under ROC after stcox / Install and uninstall @net:from http://www.stata.com/users/rgutierrez/!http://www.stata.com/users/rgutierrez/@ Materials by Roberto G. Gutierrez / Materials created by Roberto G. Gutierrez while working at StataCorp / is a set of utilities for random number generation / generates likelihood scores after clogit / generates random deviates from the binomial / distribution / calculates coverage @net:from http://www.stata.com/users/jhilbe/!http://www.stata.com/users/jhilbe/@ Materials by Joseph Hilbe, Arizona State University / The following programs were supplied by Joseph Hilbe, who retired from / the University of Hawaii in 1990 and since 1992 served as an adjunct / professor at Arizona State University. Dr. Hilbe was the founding editor of / the @net:from http://www.stata.com/users/jpitblado/!http://www.stata.com/users/jpitblado/@ Materials by Jeff Pitblado, StataCorp / Materials created by Jeff Pitblado while working at StataCorp / Packages identified by (version #) use tools that are not available prior to / Stata #. / Survey talk for the 2005 NASUG (version 9) / Survey talk for the 2006 Italy SUG (version 9) / @net:from http://www.graunt.cat/stata/!http://www.graunt.cat/stata/@ User-written commands by the Laboratori d'Estadistica Aplicada (UAB) / This site provides user-written commands and other materials for use with Stata. / Agreement: Bland-Altman & Passing-Bablok methods / All Possible Subsets: linear, logistic & Cox regression / Goodness of fit Chi-squared @net:from http://fmwww.bc.edu/RePEc/bocode/_/!http://fmwww.bc.edu/RePEc/bocode/_/@ module to calculates seats in party-list proportional representation / module to sort a single variable via egen / module for random number generation from the GB2, Singh-Maddala, Dagum, Fisk and Pareto distributions / module to compute relative difference between successive @net:from http://fmwww.bc.edu/RePEc/bocode/a/!http://fmwww.bc.edu/RePEc/bocode/a/@ module to estimate models with two fixed effects / module to compute unbiased IV regression / module for scatter plot with linear and/or quadratic fit, automatically annotated / module to provide Gradient Solver for Ahlfeldt & Barr (2022): The economics of skyscrapers / module to @net:from http://fmwww.bc.edu/RePEc/bocode/b/!http://fmwww.bc.edu/RePEc/bocode/b/@ module to account for changes when X2 is added to a base model with X1 / module to plot two graph types which are rooted in Bland-Altman plots using journal and paper percentiles / module to implement a backward procedure with a Rasch model / module to make daily backup of important @net:from http://fmwww.bc.edu/RePEc/bocode/c/!http://fmwww.bc.edu/RePEc/bocode/c/@ module to implement machine learning classification in Stata / module to implement machine learning classification in Stata / module to generate calendar / module to estimate proportions and means after survey data have been calibrated to population totals / module for inverse regression and @net:from http://fmwww.bc.edu/RePEc/bocode/d/!http://fmwww.bc.edu/RePEc/bocode/d/@ module to create network visualizations using D3.js to view in browser / module to produce terrible dad jokes / module to provide utilities for directed acyclic graphs / module to fit a Generalized Beta (Type 2) distribution to grouped data via ML / module to fit a Dagum distribution @net:from http://fmwww.bc.edu/RePEc/bocode/e/!http://fmwww.bc.edu/RePEc/bocode/e/@ module to estimate endogenous attribute attendance models / module to compute Extended Sample Autocorrelation Function / module to perform extreme bound analysis / module to perform Entropy reweighting to create balanced samples / module to perform entropy balancing / module to perform @net:from http://fmwww.bc.edu/RePEc/bocode/g/!http://fmwww.bc.edu/RePEc/bocode/g/@ module to provide graphics schemes for http://fivethirtyeight.com / module for generalised additive models / module to perform game-theoretic calculations / module to fit a two-parameter gamma distribution / module to compute the value of the symmetrical gamma function / module to perform @net:from http://fmwww.bc.edu/RePEc/bocode/h/!http://fmwww.bc.edu/RePEc/bocode/h/@ module to perform Hadri panel unit root test / module to compute Homoskedastic Adjustment Inflation Factors for model selection / module to randomly produce haikus / module to compute homoskedastic adjustment inflation factors for model selection / module to compute @net:from http://fmwww.bc.edu/RePEc/bocode/i/!http://fmwww.bc.edu/RePEc/bocode/i/@ module to import International Aid Transparency Initiative data / module to compute measures of interaction contrast (biological interaction) / module to compute Interaction Effects in Linear and Generalized Linear Models / module that computes models 2 and 3 of the intra-class @net:from http://fmwww.bc.edu/RePEc/bocode/l/!http://fmwww.bc.edu/RePEc/bocode/l/@ module to automatically manage datasets obtained from US Census 2000 and World Development Indicators databases / module to produce syntax to label variables and values, given a data dictionary / module to report numeric variables with values lacking value labels / module to list value labels / @net:from http://fmwww.bc.edu/RePEc/bocode/m/!http://fmwww.bc.edu/RePEc/bocode/m/@ module to implement interpoint distance distribution analysis / module to unabbreviate Global Macro Lists / module to compute the macroF evaluation criterion for multi-class outcomes / module to perform Dickey-Fuller test on panel data / module to create dot plot for summarizing pooled estimates @net:from http://fmwww.bc.edu/RePEc/bocode/n/!http://fmwww.bc.edu/RePEc/bocode/n/@ module to identify and adjust outliers of a variable assumed to follow a negative binomial distribution / module to generate graph command (and optionally graph) timeseries vs. NBER recession dating / module for fitting negative binomial distribution by maximum likelihood / module to @net:from http://fmwww.bc.edu/RePEc/bocode/o/!http://fmwww.bc.edu/RePEc/bocode/o/@ module to compute the Blinder-Oaxaca decomposition / module to compute decompositions of outcome differentials / module to compute the Blinder-Oaxaca decomposition / module to identify differences in values across observations for a variable / module to display observations of @net:from http://fmwww.bc.edu/RePEc/bocode/p/!http://fmwww.bc.edu/RePEc/bocode/p/@ module to calculate confidence limits of a regression coefficient from the p-value / module to perform Page's L trend test for ordered alternatives / module to create paired datasets from individual-per-row data / module for plots of paired observations / module to import network data in Pajek's @net:from http://fmwww.bc.edu/RePEc/bocode/q/!http://fmwww.bc.edu/RePEc/bocode/q/@ module to perform quadratic assignment procedure / module to generate quantile-quantile plot for data vs fitted beta distribution / module to implement quantile control method (QCM) via Random Forest / module to convert a raw Q-sort file into a new Q-sort file which is ready for @net:from http://fmwww.bc.edu/RePEc/bocode/r/!http://fmwww.bc.edu/RePEc/bocode/r/@ module to compute McKelvey & Zavoina's R2 / module to compute several fit statistics for count data models / module to perform Overall System (NL-SUR) System R2, Adj. R2, F-Test, and Chi2-Test / module to calculate an ordinal explained variation statistic / module to compute System R2, @net:from http://fmwww.bc.edu/RePEc/bocode/s/!http://fmwww.bc.edu/RePEc/bocode/s/@ Sequence analysis distance measures / module for Sankey diagrams / module to produce scatter plots with fit lines / module to provide a graphic scheme favored by many scientific journals / module to find the root path of a project and set it as a global variable / module to perform analyses @net:from http://fmwww.bc.edu/RePEc/bocode/t/!http://fmwww.bc.edu/RePEc/bocode/t/@ module to report Mean Comparison for variables between two groups with formatted table output in DOCX file / module to perform Tukey's Two-Way Analysis by Medians / module to produce a one-way table as a matrix / module to handle two-way tables with percentages / module to handle @net:from http://fmwww.bc.edu/RePEc/bocode/u/!http://fmwww.bc.edu/RePEc/bocode/u/@ module to provide prefix command for unicode utilities / module to perform univariate categorical goodness-of-fit tests / module to extract paradata from a string variable produced by the universal client-side paradata script / module to estimate the generalized unidiff model for @net:from http://fmwww.bc.edu/RePEc/bocode/x/!http://fmwww.bc.edu/RePEc/bocode/x/@ module to input an extended version of the auto data / module to transform the logit scores into probabilities / module to compute standardized differences for stratified comparisons via R / module to tabulate differences in predicted responses after restricted cubic spline models /