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Lasso is a machine-learning technique used for model selection, prediction, and inference.
Explore and combine the results from different studies
A new, unified suite of commands for modeling choice data.
Reporting features allow you to create Word, PDF, Excel, and HTML documents that incorporate Stata results and graphs with formatted text and tables.
Embed and execute Python code from within Stata.
Work with multiple datasets in memory. Link datasets and access variables in the datasets in memory.
Bayesian inference based on an MCMC (Markov chain Monte Carlo) sample is valid only if the Markov chain has converged.
These predictions are useful for checking model fit and for predicting out-of-sample observations.
Fit models that account for three common problems that arise in observational data—endogenous covariates, sample selection, and treatment—either alone or in combination.
The new ciwidth command performs Precision and Sample Size (PrSS) analysis, which is sample-size analysis for confidence intervals (CIs).
You can now import data stored in SAS (.sas7bdat). Dialog boxes make it easy to explore the data before importing.
You can now import data stored in SPSS (.sav). Dialog boxes make it easy to explore the data before importing.
Fit nonparametric series regressions that approximate the mean of the dependent variable using polynomials, B-splines, or splines of the covariates
The dsgnl command fits nonlinear DSGE models, which means that you no longer need to linearise the models first.
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.
Sometimes individuals make the same decision repeatedly. This is panel data. Relaxes the independence of irrelevant alternatives assumption.
Missing data is sometimes not random. Heckman selections help fit two level panel data.
New features for fitting nonlinear mixed-effects models (NLMEMs) that may include lag, lead (forward), and difference operators
Read more about heteroskedastic ordered probit models in the Stata Base Reference Manual; see [R] hetoprobit.
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.
Mata's new Quadrature() class provides adaptive Gaussian quadrature for numerically integrating univariate functions.
Specify the size of graph elements in printer points, inches, centimetres and relative sizes.
All of Stata's interface—all menus and all dialogs—is now available in Korean
Dark Mode is a color scheme that darkens background windows and controls, so it directs your focus to what you are working on.
The do file editor provides syntax highlighting for Python and Markdown, and now has auto-complete.