Lasso

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Lasso is a machine-learning technique used for model selection, prediction, and inference.

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Meta-analysis

Explore and combine the results from different studies

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Choice models

A new, unified suite of commands for modeling choice data.

Reproducible reporting

Reporting features allow you to create Word, PDF, Excel, and HTML documents that incorporate Stata results and graphs with formatted text and tables.

Python integration

Embed and execute Python code from within Stata.​

Frames - Multiple datasets in memory

Work with multiple datasets in memory.  Link datasets and access variables in the datasets in memory.

Bayesian - Multiple chains

Bayesian inference based on an MCMC (Markov chain Monte Carlo) sample is valid only if the Markov chain has converged.

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Bayesian - Predictions

These predictions are useful for checking model fit and for predicting out-of-sample observations.

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Panel data ERMs

Fit models that account for three common problems that arise in observational data—endogenous covariates, sample selection, and treatment—either alone or in combination.

Sample-size analysis for confidence intervals

The new ciwidth command performs Precision and Sample Size (PrSS) analysis, which is sample-size analysis for confidence intervals (CIs).

Import from SAS

You can now import data stored in SAS (.sas7bdat). Dialog boxes make it easy to explore the data before importing.

Import from SPSS

You can now import data stored in SPSS (.sav). Dialog boxes make it easy to explore the data before importing.

Nonparametric series regression

Fit nonparametric series regressions that approximate the mean of the dependent variable using polynomials, B-splines, or splines of the covariates

Nonlinear DSGE models

The dsgnl command fits nonlinear DSGE models, which means that you no longer need to linearise the models first.

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Multiple-group IRT models

The irt commands allow comparisons across groups. Take any of the existing irt commands, add a group(varname) option, and fit the corresponding multiple-group model.

Panel-data mixed logit

Sometimes individuals make the same decision repeatedly.  This is panel data. Relaxes the independence of irrelevant alternatives assumption.

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xtheckman

Missing data is sometimes not random.  Heckman selections help fit two level panel data.

Nonlinear mixed effects with lags, leads and differences

New features for fitting nonlinear mixed-effects models (NLMEMs) that may include lag, lead (forward), and difference operators

Hetroskedastic ordered probit

Read more about heteroskedastic ordered probit models in the Stata Base Reference Manual; see [R] hetoprobit.

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Linear programming

New Mata class LinearProgram() solves linear programs. It uses Mehrotra's (1992)interior-point method, which is faster for large problems than the traditional simplex method.

Numerical integration

Mata's new Quadrature() class provides adaptive Gaussian quadrature for numerically integrating univariate functions.

Point sizes for graphics

Specify the size of graph elements in printer points, inches, centimetres and relative sizes.

Stata in Korean

All of Stata's interface—all menus and all dialogs—is now available in Korean

Mac Dark Mode

Dark Mode is a color scheme that darkens background windows and controls, so it directs your focus to what you are working on.

Improvements in the do-file editor

The do file editor provides syntax highlighting for Python and Markdown, and now has auto-complete.