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Re: st: Stata 13 ships June 24


From   Philip Jones <pjones.statalist@gmail.com>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Stata 13 ships June 24
Date   Sun, 9 Jun 2013 20:14:41 -0400

Am I to understand that printed documentation is no longer available?
Just PDF? Thanks. Phil

On Sun, Jun 9, 2013 at 6:03 PM, William Gould, StataCorp LP
<wgould@stata.com> wrote:
> Short explanation
> -----------------
>
> Stata 13 ships June 24.
>
> You can order it starting now.
>
> Visit www.stata.com.
>
>
>
> Longer explanation
> ------------------
>
> It is a tradition that Statalist members are the first to know when we
> have a new release.
>
> Well, we have one.  I suspect the following new features will be of
> the greatest interest:
>
>    1.  Longer strings, even BLOBs (Binary Large OBjects)
>
>        The maximum length of string variables increases from 244 to
>        2-billion characters.
>
>        Yes, I'm referring to the string variables that we all have in
>        our .dta datasets.
>
>        It works like this:
>
>            str1, str2, ..., str244
>                (working just as previously)
>
>            str245, ..., str2045
>                (new and working just as you would expect)
>
>            strL
>                (new, beyond 2,045, and working just like str#)
>
>        strLs (pronounced sturls) can contain ascii or binary.
>
>        strLs are coalesced, meaning the same copy of a strL is shared
>        across observations to save memory.
>
>        You work with strLs the same as you work with str# variables.
>        All of Stata functions and commands work with them.  This
>        includes -substr()-, -generate-, -replace-, -by-, -sort-,
>        -tabulate-, etc.  The exception is that you cannot use a
>        strL variable as a key variable in a -merge-.
>
>        Programmers:  strLs can be longer than local and global macros,
>        which have a maximum length of (only) 1,081,511 characters.
>        That means that all of Stata's string functions now work with
>        macros!
>
>
>    2.  Treatment-effects estimators.
>
>        Treatment-effects estimators measure the causal effect of
>        treatment on an outcome in observational data.
>
>        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.
>
>        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
>        to be right about either the specification of outcome or the
>        specification of participation, but not both.
>
>        Treatment effects are the subject of the all-new _Stata
>        Treatment-Effects Reference Manual_.
>
>
>    3.  Endogenous treatment-effect estimators
>
>        As I said, treatment effects measure the causal effect of
>        treatment on outcome.  Sometimes we do not have conditional
>        independence, which is to say, unobserved variables affect both
>        treatment and outcome.
>
>        The new endogenous treatment estimators address such cases.
>
>        -etregress- handles continuous outcome variables.
>
>        -etpoisson- handles count outcomes.
>
>        (-etregress- is an updated form of old command -treatreg-; -
>        etpoisson- is new.)
>
>
>    4.  Multilevel mixed-effects and generalized linear
>        structural-equation modeling.
>
>        Existing command -sem- fits linear SEMs.
>
>        New command -gsem- joins -sem- and fits generalized SEMs.
>
>        Generalized SEMs is a term we have coined to mean
>        generalized linear response functions and to mean nested and
>        crossed effects, which can be used together or separately.
>
>        Generalized linear response functions include linear regression,
>        naturally enough, and they include probit, logit, complementary
>        log-log, Poisson, negative binomial. multinomial logit, ordered
>        probit, ordered logit, and more.
>
>        Nested and crossed effects means latent variables at different
>        levels of the data.  2 levels.  3 levels.  More levels.
>
>        -gllamm- users:  There is a lot of overlap in the models that
>        -gllamm- and -gsem- can fit.  When there is overlap, -gsem- is
>        much faster.
>
>
>    5.  More multilevel mixed-effects models.
>
>        Stata already had multilevel mixed-effects linear, logistic, and
>        Poisson regression.
>
>        Now we also have probit, complementary log-log, ordered
>        logistic, ordered probit, negative binomial, and generalized
>        linear models.
>
>        And all the commands -- even the existing ones -- now allow
>        constraints on variance components and can provide robust and
>        cluster-robust standard errors.
>
>        And the new commands are not only faster, they are bordering on
>        fast.
>
>        Mixed-effects regression now has its own manual.
>
>
>    6.  Forecasts.
>
>        The new -forecast- command lets you combine results from
>        multiple Stata estimation commands and/or other sources to
>        produce dynamic or static forecasts and forecast intervals.
>
>        Specify models.  Specify identities.  Obtain baseline forecast.
>        Specify alternative paths.  Obtain forecast.  That means
>        forecasts under alternative scenarios and ability to explore
>        impacts of differing policies.  Especially useful for
>        macroeconomic forecasts.
>
>
>    7.  Power and sample size
>
>        Solve for power, sample size, minimum detectable effect, or
>        effect size.
>
>        Comparisons of means (t tests), proportions, variances, correlations.
>
>        Matched case-control studies, cohort studies, cross-sectional studies.
>
>        Standard and customizable tables and graphs.
>
>        And its own manual.
>
>
>    8.  New and extended random-effects panel-data estimators.
>
>        Ordered probit and ordered logistic join the existing
>        random-effects panel-data estimators linear regression,
>        interval-data regression, tobit, probit, logistic, complementary
>        log-log, and Poisson.
>
>        Robust standard errors to relax distributional assumptions.
>        Cluster-robust standard errors for correlated data.
>
>
>    9.  Effects sizes.
>
>        Results the way behavioral scientists and especially
>        psychologists want to see them.
>
>        Comparison of means:  Cohen's d, Hedges's g, Glass's Delta,
>        point/biserial correlation.  Estimated from data or from
>        published summary statistics.
>
>        Variances explained by regression and ANOVA: Eta-squared and
>        partial eta-squared, omega-squared and partial omega-squared.
>        Partial statistics estimated from data.  Overall statistics from
>        data or from published summary statistics.
>
>
>   10.  Project Manager.
>
>        Organize any kind of file (do-files, ado-files, datasets, raw
>        files, etc.) into hierarchical list for quick access.
>        Manage hundreds, even thousands, of files per project.
>
>        Manage multiple projects.
>
>        Create groups in project to categorize files.
>
>        Click to open datasets, display saved graphs, open do-files in the
>        Do-file Editor.
>
>        Rename file.  Filter on filenames. Search for file using keywords.
>
>        You have to try it to appreciate it, but in the meantime, you
>        can find pictures at www.stata.com.
>
>
>    11. Java plugins.
>
>        Call Java methods directly from Stata.  Interact with Stata's
>        datasets, matrices, macros, etc.  Take advantage of existing
>        Java libraries, or write your own code.
>
>
>    12. And more
>
>        I should mention the improved help-file searching, and that
>        Stata now supports secure HTTP and FTP, and fast PDF manual
>        navigation, and ordered probit with Heckman-style sample
>        selection, and the new way of estimating ML models without
>        writing an evaluator program, and the new fractional-polynomial
>        prefix command, and that quantile regression can now produce
>        robust estimates of standard errors, and that factor variables
>        now support value labels for labeling output, and the new way
>        to import data from Haver Analytics, and automatic
>        business-calendar creation, and the new import commands that
>        make reading data really easy, and how you can create Word and
>        Excel files from Stata, and solve arbitrary nonlinear systems,
>        and a lot of other things.
>
>
> I could go on, but instead I'll mention that we have finally implemented the
> feature that is the most requested at user meetings around the world:
>
> You can now type -cls- to clear the Results Window.
>
> -- Bill
> wgould@stata.com
>
>
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