Stata 15 help for whatsnew12to13

What's new in release 13.0 (compared with release 12)

This file lists the changes corresponding to the creation of Stata release 13.0:

+---------------------------------------------------------------+ | help file contents years | |---------------------------------------------------------------| | whatsnew Stata 15.0 and 15.1 2017 to present | | whatsnew14to15 Stata 15.0 new release 2017 | | whatsnew14 Stata 14.0, 14.1, and 14.2 2015 to 2017 | | whatsnew13to14 Stata 14.0 new release 2015 | | whatsnew13 Stata 13.0 and 13.1 2013 to 2015 | | this file Stata 13.0 new release 2013 | | whatsnew12 Stata 12.0 and 12.1 2011 to 2013 | | whatsnew11to12 Stata 12.0 new release 2011 | | whatsnew11 Stata 11.0, 11.1, and 11.2 2009 to 2011 | | whatsnew10to11 Stata 11.0 new release 2009 | | whatsnew10 Stata 10.0 and 10.1 2007 to 2009 | | whatsnew9to10 Stata 10.0 new release 2007 | | whatsnew9 Stata 9.0, 9.1, and 9.2 2005 to 2007 | | whatsnew8to9 Stata 9.0 new release 2005 | | whatsnew8 Stata 8.0, 8.1, and 8.2 2003 to 2005 | | whatsnew7to8 Stata 8.0 new release 2003 | | whatsnew7 Stata 7.0 2001 to 2002 | | whatsnew6to7 Stata 7.0 new release 2000 | | whatsnew6 Stata 6.0 1999 to 2000 | +---------------------------------------------------------------+ -------------------------------------------------------------------------------

Most recent changes are listed first.

--- more recent updates -------------------------------------------------------

See whatsnew13.

--- Stata 13.0 release 17jun2013 ----------------------------------------------


We will list all the changes, item by item, but first, here are the highlights.

What's new (highlights)

Here are the highlights. There are more, and do not assume that because we mention a category, we have mentioned everything new in the category. Detailed sections follow the highlights.

1. Long strings/BLOBs. The maximum length of string variables increases from 244 to 2,000,000,000 characters. The standard string storage types str1, str2, ..., str244 now continue to str2045, and after that comes strL, pronounced sturl. All of Stata's string functions work with two-billion-character-long strings, as do the rest of Stata's features, including importing, exporting, and ODBC. strL variables can contain binary strings. New functions, fileread() and filewrite(), make it easy to read and write entire files to and from strLs.

See [U] 12.4 Strings.

(BLOB stands for binary large object, jargon used by database programmers.)

2. Treatment effects. A new suite of features allows you to estimate average treatment effects (ATE), average treatment effects on the treated (ATET), and potential-outcome means (POMs). Binary, multilevel, and multivalued treatments are supported. You can model outcomes that are continuous, binary, count, or nonnegative.

Treatment-effects estimators measure the causal effect of treatment on an outcome in observational data.

Different treatment-effects estimators are provided for different situations.

When you know the determinants of participation (but not the determinants of outcome), inverse-probability weights (IPW) and propensity-score matching are provided.

When you know the determinants of outcome (but not the determinants of participation), regression adjustment and covariate matching are provided.

When you know the determinants of both, the doubly robust methods augmented IPW and IPW with regression adjustment are provided. These methods are doubly robust because you need to be right about only the specification of outcome, or of participation.

Also provided are two estimators that do not require conditional independence. Conditional independence means that the treatment and observed outcome are uncorrelated conditional on observed covariates. Put another way, conditional independence implies selection on observables. New estimation commands etregress and etpoisson relax the assumption. (etregress is an updated form of old command treatreg; etpoisson is new.)

See the all-new Stata Treatment-Effects Reference Manual, and in particular, see [TE] teffects intro.

By the way, if treatment effects interest you, also see [SEM] example 46g, where we use gsem -- another new feature of Stata 13 -- to fit an endogenous treatment-effects model that can be modified to allow for generalized linear outcomes and multilevel effects.

3. Multilevel mixed effects and generalized linear structural equation modeling (SEM). In addition to standard linear SEMs, Stata now provides what we are calling generalized SEMs for short. Generalized SEMs allow for generalized linear response functions and allow for multilevel mixed effects.

Generalized linear response functions include binary outcomes (probit, logit, cloglog), count outcomes (Poisson, negative binomial), categorical outcomes (multinomial logit), ordered outcomes (ordered probit, ordered logit, ordered cloglog), and more, which is to say, generalized linear models (GLMs).

Multilevel mixed effects include nested random effects such as effects within patient within doctor within hospital and crossed random effects. Multilevel mixed effects also include random intercepts and random slopes.

In the language of SEM, "multilevel mixed effects" means latent variables at different levels of the data. This means Stata 13 can fit multilevel measurement models and multilevel structural equation models.

See [SEM] intro 1.

Economists: See [SEM] example 45g, where we show how to use Stata 13's new SEM features to fit the Heckman selection model, which can be extended to generalized linear outcomes and random effects and random slopes.

4. New multilevel mixed-effects models. Multilevel mixed-effects estimation has been improved and expanded and is now the subject of its own manual. Stata had 3 multilevel estimation commands; now it has 11.

The eight new multilevel mixed-effects estimation commands are

melogit logistic regression meprobit probit regression mecloglog complementary log-log regression meologit ordered logistic regression meoprobit ordered probit regression mepoisson Poisson regression menbreg negative binomial regression meglm generalized linear models

These new estimation commands allow for constraints on variance components, provide robust and cluster-robust standard errors, and are fast.

The three existing multilevel estimation commands have been renamed: xtmixed is now mixed, xtmelogit is now meqrlogit, and xtmepoisson is now meqrpoisson. All three now present results by default in the variance metric rather than the standard deviation metric.

As we said, multilevel mixed-effects modeling is now the subject of its own manual. See Stata Multilevel Mixed-Effects Reference Manual, and in particular, see [ME] me.

5. Forecasts based on systems of equations. Stata's new forecast command allows you to combine estimation results from multiple Stata commands or other sources to produce dynamic or static forecasts and produce forecast intervals.

You begin by fitting the equations of your model using Stata's estimation commands, or you can enter results that you obtained elsewhere. Then you use forecast to specify identities and exogenous variables to obtain a baseline forecast. Once you produce the baseline forecast, you can specify alternative paths for some variables and obtain forecasts based on those alternative paths. Thus you can produce forecasts under alternative scenarios and explore impacts of differing policies.

You can use forecast, for example, to produce macroeconomic forecasts.

In addition, forecast is particularly easy to use because forecast also provides an intuitive, interactive control panel to guide you and, if you do something wrong, forecast itself offers advice on how to fix the problem.

See [TS] forecast.

6. Power and sample size. The new power command performs power and sample-size analysis. Included are

Comparison of a mean to a reference value Comparison of a proportion to a reference value Comparison of a variance to a reference value Comparison of a correlation to a reference value

Comparison of two independent means Comparison of two independent proportions Comparison of two independent variances Comparison of two independent correlations

Comparison of two paired means Comparison of two paired proportions

Results can be displayed in customizable tables and graphs.

An integrated GUI lets you select your analysis type, input assumptions, and obtain desired results.

Power and sample size is the subject of its own manual. See Stata Power and Sample-Size Reference Manual; start by seeing [PSS] intro.

7. New and extended panel-data estimators. Two new random-effects panel-data estimation commands are added:

xtoprobit ordered probit regression xtologit ordered logistic regression

These new commands allow for cluster-robust standard errors.

The following previously existing random-effects panel-data estimation commands now allow for cluster-robust standard errors:

xtprobit probit regression xtlogit logistic regression xtcloglog complementary log-log regression xtpoisson Poisson regression

See [XT] xt for a complete list of all of Stata's panel-data estimators.

8. New commands are provided for calculating effect sizes after estimation in the way behavioral scientists, and especially psychologists, want to see them. Cohen's d, Hedges's g, Glass's Delta, eta^2, and omega^2, with confidence intervals, are now provided:

a. New commands esize and esizei calculate effect sizes comparing the difference between the means of a continuous variable for two groups. See [R] esize. {phang3} b. New postestimation command estat esize computes effect sizes for linear models after anova and regress. See [R] regress postestimation. {phang2} 9. Project Manager.{break} The new Project Manager lets you organize your analysis files -- your do-files, ado-files, datasets, raw files, etc. You can have multiple projects, and each can contain hundreds of files, or just a few. {pmore2} You can see all the files in a project at a glance, filter on filenames, and click to open, edit, or run. {pmore2} Projects are portable, meaning that you can pick the whole collection up at once and move it across computers or share it with colleagues. {pmore2} Try it. Get started from the Do-file Editor by selecting File > New > Project ... {pmore2} See [P] Project Manager. {p 7 12 2} 10. Java plugins. {break} You can now call Java methods directly from Stata. You can take advantage of the plethora of existing Java libraries or write your own Java code. You call Java using Stata's new javacall command. See [P] java and see the Java-Stata API specification at {pmore2} Java recently encountered some negative publicity regarding security concerns. That publicity was about Java and web browsers automatically loading and running Java code from untrusted websites. It does not apply to Stata's implementation of Java. Stata's implementation is about running Java code already installed on your computer from known and trusted sources. What's new that you will want to know {p 7 12 2} 11. You can clear the Results window.{break} Use the new cls command. See [R] cls. {p 7 12 2} 12. Value labels of factor variables used to label output.{break} You use variable, and output now shows male and female in your model rather than 0 and 1 if variable sex has a value label. You can control how output looks. See more details below in [U] 1.3.3 What's new in statistics (general). {p 7 12 2} 13. Programmers can create Word and Excel files from Stata.{break} You can add paragraphs, insert images, insert tables, poke into individual cells, and more. {pmore2} See [M-5] _docx*() to create Word documents. {pmore2} See [P] putexcel and [M-5] xl() to interact with Excel files. {pmore2} By the way, Stata could already import and export Excel files; see [D] import excel. {p 7 12 2} 14. Searching is better.{break} Here's why: {phang3} a. Help > Search... and the search command now default to searching the Internet as well as Stata's local keyword database. If you do not want that, type set searchdefault local, permanently to set Stata 13 to the old default. {phang3} b. search without options now displays its results in the Viewer rather than in the Results window. (If any options are specified, however, results appear in the Results window.) {phang3} c. Existing command findit is no longer documented but continues to work. Changes to search make search into the equivalent of findit. {pmore2} See [R] search. {p 7 12 2} 15. help now searches when no help is found.{break} help xyz now invokes search xyz if xyz is not found. See [R] help. {p 7 12 2} 16. Stata now supports secure HTTP (HTTPS) and FTP. You can, for instance, use datasets from sites using either of the protocols. See [U] 3.5 Updating and adding features from the web. {p 7 12 2} 17. Concerning the Data Editor, {phang3} a. noncontiguous column selections are now allowed. {phang3} b. encode, decode, destring, and tostring have been added as operations that can be performed on selected variables. {phang3} c. the Delete key can now be used to drop data. {pmore2} See [GS] 6 Using the Data Editor (GSM, GSU, or GSW). {p 7 12 2} 18. Concerning the Do-file Editor, {phang3} a. matching braces are highlighted. {phang3} b. an adjustable column guide has been added. {phang3} c. you can now zoom in and out. {phang3} d. you can convert between the different types of end-of-line characters used by Windows and by Mac and Unix. {pmore2} See [GS] 13 Using the Do-file Editor---automating Stata (GSM, GSU, or GSW). {p 7 12 2} 19. Concerning Stata's GUI, {phang3} a. the Properties window now displays the sorted-by variables. {phang3} b. the Jump To menu in the Viewer now allows you to jump to the top of the page. {phang3} c. Stata for Windows now supports Windows high-contrast themes. {p 7 12 2} 20. .dta file format has changed.{break} The file format has changed because of the new strL variables. Stata 13 can, of course, read old-format datasets. If you need to create datasets in the previous format -- used by Stata 11 and Stata 12 -- use the saveold command. See [D] save. If you want to know the details of the new .dta format, type help dta. {p 7 12 2} 21. Official directory ado/updates no longer used.{break} Official ado-file updates are no longer stored in directory installation-directory/ado/updates/. Updates are now applied to ado/base directly. Modern operating systems do not approve of applications such as Stata having multiple files of the same name. The updates process remains the same. {p 7 12 2} 22. Videos.{break} Type help videos to list and link to the videos on Stata's YouTube channel. We provide dozens of tutorials on Stata's features. {p 7 12 2} 23. Fast PDF-manual navigation.{break} There are now links at the top of each manual entry to jump directly to section headings, and on each page's header, there is a link to take you to the beginning of the entry. {pmore2} If you did not know already, clicking on the blue manual reference in the title of a help file jumps to the PDF documentation. {p 7 12 2} 24. Manuals have color graphs.{break} If you want to use the same color graph scheme we use in the manuals, type set scheme s2gcolor. See [G-4] scheme s2. {p 7 12 2} 25. Ten new vignettes.{break} Scientific history buffs will want to read about the following: {phang3} a. Florence Nightingale {phang3} b. Florence Nightingale David, a different person from Florence Nightingale {phang3} c. Charles William Dunnett {phang3} d. Andrew Charles Harvey {phang3} e. William Lee Hays {phang3} f. Fred Nichols Kerlinger {phang3} g. Janet Elizabeth Lane-Claypon {phang3} h. martingale {phang3} i. Elizabeth L. "Betty" Scott {phang3} j. John Snow {pstd} The following two items were added during the Stata 12 release: {p 7 12 2} 26. New command icc computes intraclass correlation coefficients for one-way random-effects models, two-way random-effects models, and two-way mixed-effects models for both individual and average measurements. Intraclass correlations measure consistency of agreement or absolute agreement. See [R] icc. {p 7 12 2} 27. New postestimation command estat icc computes intraclass correlations at each nesting level for nested random-effects models fit by mixed and melogit. See [ME] mixed postestimation and [ME] melogit postestimation. What's new in statistics (general) {pstd} Already mentioned as highlights of the release were treatment effects, generalized SEMs, multilevel mixed-effects models, power and sample size, and panel-data estimators. The following are also new: {p 7 12 2} 28. Concerning sample-selection estimation commands, {phang3} a. new estimation command heckoprobit fits the parameters of an ordered probit model with sample selection. See [R] heckoprobit. {phang3} b. existing estimation command heckprob is renamed heckprobit. See [R] heckprobit. {p 7 12 2} 29. Existing estimation command hetprob is renamed hetprobit. See [R] hetprobit. {p 7 12 2} 30. New estimation command ivpoisson fits the parameters of a Poisson regression model with endogenous regressors. Estimates can be obtained using the GMM or control-function estimators. See [R] ivpoisson. {p 7 12 2} 31. New command mlexp allows you to specify maximum likelihood models without writing an evaluator program. You can instead specify an expression representing the log-likelihood function in much the same way you would with nl, nlsur, or gmm. See [R] mlexp. {p 7 12 2} 32. Concerning fractional polynomials, {phang3} a. new prefix command fp: replaces fracpoly for fitting models with fractional polynomial regressors. You type . fp ...: estimation command {pmore2} Results are the same. The new fp command supports more estimation commands, it is easier to use, and it is more flexible. You can substitute the same fractional polynomial into multiple places of the estimation command, which is especially useful in multiple-equation models. You may now use factor-variable notation in the estimation command. {phang3} b. fp generate replaces fracgen. {phang3} c. fp plot replaces fracplot. {phang3} d. fp predict replaces fracpred. {phang3} e. commands fracpoly and fracgen are no longer documented but continue to work. Commands fracplot and fracpred are still documented for use after mfp. {pmore2} See [R] fp. {p 7 12 2} 33. Concerning quantile-regression estimation commands, {phang3} a. existing estimation command qreg now accepts option vce(robust). {phang3} b. existing estimation commands qreg, iqreg, sqreg, and bsqreg now allow factor variables to be used. {pmore2} See [R] qreg. {p 7 12 2} 34. Syntax and methodology for predict after boxcox have changed. Predicted values are now calculated using Duan's smearing method by default. The previous back-transformed predicted-values estimates are provided if predict's btransform option is specified and under version control. See [R] boxcox postestimation. {p 7 12 2} 35. Value labels of factor variables are now used by default to label estimation output. The numeric values (levels) were previously used and continue to be used if the factor variables are unlabeled. There are three new display options that may be used with estimation commands affecting how this works: {phang3} a. Option nofvlabel displays factor-variable level values, just as Stata 12 did previously. (You can set fvlabel off to make nofvlabel the default.) {phang3} b. Option fvwrap(#) specifies the number of lines to allow when long value labels must be wrapped. Labels requiring more than # lines are truncated. fvwrap(1) is the default. You can change the default by using set fvwrap #. {phang3} c. Option fvwrapon() specifies whether value labels that wrap will break at word boundaries. {pmore3} fvwrapon(word) is the default, meaning to break at word boundaries. {pmore3} fvwrapon(width) specifies that line breaks may occur arbitrarily so as to maximize use of available space. {pmore3} You can change the defaults by using set fvwrapon width or set fvwrapon word. {pmore2} Current default settings are shown by query and also stored in c(fvlabel), c(fvwrap), and c(fvwrapon). {pmore2} See [R] set showbaselevels and [P] creturn. {p 7 12 2} 36. Existing estimation command proportion now uses the logit transform when computing the limits of the confidence interval. The original behavior of using the normal approximation is preserved under version control or when the new citype(normal) option is specified. See [R] proportion. {p 7 12 2} 37. Concerning existing command margins, {phang3} a. option at() has new suboption generate(), which allows you to specify an expression to replace the values for any continuous variable in the model. For example, you can compute the predictive margins at x+1 by typing . margins, at(x = generate(x+1)) {pmore3} at(generate()) can be combined with contrasts to estimate the effect of giving each subject an additional amount of x, . margins, at((asobserved) _all) at(x= generate(x+1)) /// contrast(at(r._at)) {pmore3} See Estimating treatment effects with margins in [R] margins, contrast. {phang3} b. margins automatically uses the t distribution for computing p-values and confidence intervals when appropriate, which is after linear regression and ANOVA and whenever degrees of freedom are posted to e(df_r). {pmore3} The previous default behavior of always using the standard normal distribution for all p-values and confidence intervals is preserved under version control. {phang3} c. new option df(#) specifies that margins is to use the t distribution when it otherwise would not. {pmore2} See [R] margins. {p 7 12 2} 38. nlcom and predictnl now use the standard normal distribution for computing p-values and confidence intervals. Original behavior was to compute the p-values and CIs based on the t distribution in some cases. Original behavior is preserved under version control. In addition, if you want p-values and confidence intervals calculated using the t distribution, use new option df(#) to specify the degrees of freedom. {pmore2} testnl's calculated test statistic is now chi-squared rather than F unless you specify the df() option. {pmore2} See [R] nlcom, [R] predictnl, and [R] testnl. {p 7 12 2} 39. contrast, pwcompare, and lincom have new option df(#) to use the t distribution in computing p-values and confidence intervals. For contrast, this option also causes the Wald table to use the F distribution. {pmore2} See [R] contrast, [R] pwcompare, and [R] lincom. {p 7 12 2} 40. estimates table's option label is renamed varlabel. Original option label is allowed under version control. See [R] estimates table. {p 7 12 2} 41. The previously existing sampsi command is no longer documented because it is replaced by the new power command -- a highlight of the release. See [PSS] power. {p 7 12 2} 42. Existing functions normalden(x,mu,sigma) and lnnormalden(x,mu,sigma) now allow you to omit argument mu or arguments mu and sigma. mu=0 and sigma=1 is assumed. See normalden(), lnnormalden(), and [FN] Functions by category. {p 7 12 2} 43. The following new functions are added: {synopt:t(df,t)}cumulative Student's t distribution{p_end} {synopt:invt(df,p)}inverse cumulative Student's t distribution{p_end} {synopt:ntden(df,np,t)}density of noncentral Student's t distribution{p_end} {synopt:nt(df,np,t)}cumulative noncentral Student's t distribution{p_end} {synopt:npnt(df,t,p)}noncentrality parameter of noncentral Student's t distribution{p_end} {synopt:nttail(df,np,t)}right-tailed noncentral Student's t distribution{p_end} {synopt:invnttail(df,np,p)}inverse of right-tailed noncentral Student's t distribution{p_end} {synopt:nF(df_1,df_2,np,f)}cumulative noncentral F distribution{p_end} {synopt:npnF(df_1,df_2,f,p)}noncentrality parameter of noncentral F distribution{p_end} {synopt:chi2den(df,x)}density of chi-squared distribution{p_end} {synopt:fileread(f)}return the contents of a file as a string{p_end} {synopt:filewrite(f,s[,r])}create or overwrite file with the contents of a string{p_end} {synopt:fileexists(f)}check whether a file exists{p_end} {synopt:filereaderror(s)}use results returned by fileread() to determine whether an I/O error occurred{p_end} {pmore2} See help functionname() and [FN] Functions by category. What's new in statistics (SEM) {pstd} We have already mentioned a highlight of the release, the new gsem (see [SEM] intro 1) command, for fitting generalized SEMs. The following are also new: {p 7 12 2} 44. Existing estimation command sem has new option noestimate, which is useful when you are having convergence problems; you can use it to get the starting values into a Stata matrix (vector) that you can then modify to use as alternative starting values. See [SEM] intro 12. {p 7 12 2} 45. sem now supports time-series operators on all observed variables. See [SEM] sem. {p 7 12 2} 46. You can now use postestimation command margins after sem. See [SEM] intro 7. {p 7 12 2} 47. sem no longer reports in the estimation output any zero-valued constraints on covariances between exogenous variables; absence of the covariance indicates the presence of the constraint. Original behavior is preserved under version control. {p 7 12 2} 48. The new options for controlling display of factor variables with value labels mentioned in [U] 1.3.3 What's new in statistics (general) -- nofvlabel, fvwrap(#), and fvwrapon(word|width) -- work with varname of sem, group(varname). sem itself does not allow factor variables, but the factor-variable display options nonetheless work with group(varname). {pmore2} Thus old options wrap() and nolabel are now officially fvwrap() and fvnolabel, although the old option names continue to work as synonyms. See [SEM sem reporting options. {p 7 12 2} 49. We now show how to construct path diagrams at the end of each estimation example in the manual. See [SEM] example 1, [SEM] example 3, .... What's new in statistics (time series) {pstd} We have already mentioned a highlight of the release, the new [TS] forecast command. The following are also new: {p 7 12 2} 50. New command import haver (available with Stata for Windows only) replaces old command haver. import haver imports economic and financial data from Haver Analytics databases. See [D] import haver. {p 7 12 2} 51. Existing command tsreport now provides better information about gaps in time-series and panel datasets, including the length of each gap. {pmore2} In addition, tsreport will provide information about missing values in variables even where there are no gaps. {pmore2} See [TS] tsreport. {pmore2} Also see item 55 in [U] 1.3.8 What's new in data management for information on the new command bcal create. What's new in statistics (longitudinal/panel data) {p 7 12 2} We have already mentioned a highlight of the release, new and extended panel-data estimators. What's new in statistics (survival analysis) {p 7 12 2} 52. Shared frailty survival models can no longer be fit when there is delayed entry or there are gaps in time under observation. Said differently, stcox and streg no longer allow option shared() when there are delayed entry or gaps. The use of shared frailty models to fit truncated survival data leads to inconsistent results unless the frailty distribution is independent of the covariates and the truncation point, which rarely happens in practice. If you have such data and can make the independence assumption -- which is unlikely -- estimation can be forced by specifying undocumented option forceshared. See [ST] stcox and [ST] streg. See st_forceshared for information on the forceshared option. {p 7 12 2} 53. Output produced by existing commands stset, streset, and cttost more accurately labels time at risk. What was labeled "total time at risk" is now labeled "total time at risk and under observation". See [ST] stset and [ST] cttost. What's new in data management {pstd} We have already mentioned a highlight of the release, long strings/BLOBs. {p 7 12 2} 54. New commands import delimited and export delimited supersede old commands insheet and outsheet. This is not just a renaming. {pmore2} import delimited supports several different quoting methods. Some packages, for instance, use "" in the middle of a string to represent an embedded double quote. Others do not. {pmore2} import delimited now allows column and row ranges (subsets). {pmore2} Use import delimited's GUI to see a preview of the data and how they will be read. You can also customize the GUI. {pmore2} Of course, import delimited and export delimited support Stata 13's new strLs. {pmore2} See [D] import delimited. {p 7 12 2} 55. existing command bcal has new subcommand create to create a business calendar from the current dataset automatically. bcal create infers business holidays and closures from gaps in the data. See [D] bcal. {p 7 12 2} 56. String expressions now support string duplication via multiplication. For example, 3*"abc" evaluates to "abcabcabc". See strdup() or [FN] Functions by category. {p 7 12 2} 57. Concerning long strings, that is, strLs, {phang3} a. existing command compress has new option nocoalesce in support of the new strL string storage type. By default, compress coalesces the storage used to store duplicated strL values. nocoalesce prevents this. {pmore3} In addition, compress always considers demoting strL variables to str# variables if that would save memory. {pmore3} See [D] compress. {phang3} b. the output of existing command memory has changed to include information on new string storage type strL. See [D] memory. {phang3} c. the options of existing command ds, such as has() and not(), now understand string to mean both strL and str#, strL to mean strL, and str# to mean str1, str2, ..., str2045. See [D] ds. {phang3} d. existing command type has new option lines(#) to list the first # lines of the file. See [D] type. {pmore2} Also see item 50 in [U] 1.3.5 What's new in statistics (time series) for information on the new command import haver. What's new in Mata {p 7 12 2} 58. Programmers can create Word and Excel files from Stata.{break} You can add paragraphs, insert images, insert tables, poke into individual cells, and more. {pmore2} See [M-5] _docx*() to create Word documents. {pmore2} See [P] putexcel and [M-5] xl() to interact with Excel files. {pmore2} By the way, Stata could already import and export Excel files; see [D] import excel. {p 7 12 2} 59. New functions in solvenl() allow you to solve arbitrary systems of nonlinear equations. Gauss--Seidel, damped Gauss--Seidel, Broyden--Powell, and Newton--Raphson techniques are provided. See [M-5] solvenl(). {p 7 12 2} 60. The same statistical functions added to Stata have been added to Mata, namely, Noncentral Student's t p = nt(df, np, t) d = ntden(df, np, t) q = nttail(df, np, t) t = invnttail(df, np, q) np = npnt(df, t, p) Student's t p = t(df, t) t = invt(df, p) Noncentral F p = nF(df_1, df_2, np, f) np = npnF(df_1, df_2, f, p) chi-squared d = chi2den(df, x) {pmore2} See [M-5] normal(). {p 7 12 2} 61. New function selectindex() returns a vector of indices for which v[j] not equal 0. For instance, if v = (6, 0, 7, 0, 8), then selectindex(v) = (1, 3, 5). selectindex() is useful with logical expressions, such as x[selectindex(x:>1000)]. See [M-5] select(). What's new in programming {pstd} We have already mentioned the Project Manager and Java plugins as highlights of the release. The following are also new: {p 7 12 2} 62. New command putexcel writes Stata expressions, matrices, and stored results to an Excel file. Excel 1997/2003 (.xls) files and Excel 2007/2010 (.xlsx) files are supported. See [P] putexcel. {pmore2} Mata programmers will also be interested in [M-5] xl(), a class to interact with Excel files. {p 7 12 2} 63. A new set of Mata functions provide the ability to create Word documents. See [M-5] _docx*(). {p 7 12 2} 64. Concerning strLs, {phang3} a. strL is now a reserved word. {phang3} b. the maximum length of a string in string expressions increases from 244 to 2-billion characters. See limits. {phang3} c. new c(maxstrlvarlen) returns the maximum possible length for strL variables. {phang3} d. confirm ... variable now understands str# to mean any str1, str2, ..., str2045 variable; strL to mean strL; and string to mean str# or strL. See [P] confirm. {phang3} e. new function fileread(filename [, startpos [, length]]) returns the contents of filename. See fileread() and [FN] Functions by category. {phang3} f. new function filewrite(filename, s [, {1|2}]) writes s to the specified filename, optionally overwriting 1 or appending 2. See filewrite() and [FN] Functions by category. {phang3} g. new function fileexists(filename) returns 1 if the specified filename exists, and returns 0 otherwise. {phang3} h. new function filereaderror(s) returns 0 or a positive integer, said value having the interpretation of a return code. It is used like this {p 16 18 2} . generate strL s = fileread(filename) if fileexists(filename){p_end} {p 16 18 2} . assert filereaderror(s)==0 {pmore3}or this {p 16 18 2} . generate strL s = fileread(filename) if fileexists(filename){p_end} {p 16 18 2} . generate rc = filereaderror(s) {pmore3} That is, filereaderror(s) is used on the result returned by fileread(filename) to determine whether an I/O error occurred. {pmore3} In the example, we only fileread() files that fileexist(). That is not required. If the file does not exist, that will be detected by filereaderror() as an error. The way we showed the example, we did not want to read missing files as errors. If we wanted to treat missing files as errors, we would have coded {p 16 18 2} . generate strL s = fileread(filename){p_end} {p 16 18 2} . assert filereaderror(s)==0 {pmore3} or {p 16 18 2} . generate strL s = fileread(filename){p_end} {p 16 18 2} . generate rc = filereaderror(s) {p 7 12 2} 65. New command expr_query exp returns in r() the variables used in expression exp. See undocumented and see expr_query. {p 7 12 2} 66. The maximum number of elements in a numlist increases from 1,600 to 2,500. See [U] 11.1.8 numlist. {p 7 12 2} 67. Existing command ereturn post now allows posting of noninteger as well as integer dof() values. {p 7 12 2} 68. New c(hostname) returns the computer's hostname. See [P] creturn. {p 7 12 2} 69. New c(maxvlabellen) returns the maximum possible length for a value label. What's new, Mac only {pstd} In addition to all the above What's New items, which apply to all platforms, Stata for Mac has several of its own new features: {p 7 12 2} 70. The Do-file Editor in Stata for Mac has been completely rewritten. It now includes {phang3} o code folding {phang3} o more robust syntax highlighting that is consistent with highlighting in Windows and Unix {phang3} o more color options for customizing its appearance {phang3} o the ability to save the syntax-highlighting colors as separate themes {phang3} o line ending preservation and normalization, which is useful for working in a mixed platform environment where do-files are exchanged between Windows and Macs {phang3} o text-size zooming without having to change the font or font size {phang3} o more drag-and-drop options {phang3} o more control over the appearance of printed files {p 7 12 2} 71. The Command window now has the same syntax highlighting as the Do-file Editor. {p 7 12 2} 72. There is a new path control that not only shows the current working directory but also can change the current working directory and open Stata files without having to use the Open dialog. {p 7 12 2} 73. Mac OS X 10.7 GUI enhancements such as full-screen support and textured backgrounds for spring-back scrolling are now supported. {p 7 12 2} 74. There is a new interface for saving and managing saved preferences. {p 7 12 2} 75. Applescript is better supported and enables users to directly access Stata macros, scalars, stored results, and datasets. {p 7 12 2} 76. Stata for Mac is now 64-bit only and allows the application's file size to be roughly 67% smaller. What's more {pstd} We have not listed all the changes, but we have listed the important ones. {pstd} Stata is continually being updated. Those between-release updates are available for free over the Internet. {pstd} Type update query and follow the instructions. {pstd} We hope that you enjoy Stata 13. -------- previous updates {hline} {pstd} See whatsnew12.{p_end} {hline}

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