Stata MP 16.0

Description

Stata 16 is a big release, which our releases are usually. This one is broader than usual. It ranges from lasso to Python and from multiple datasets in memory to multiple chains in Bayesian analysis. The highlights are listed below. If you click on a highlight, we will spirit you away from our website, where we will describe the feature in a dry but information-dense way. Or you can scroll down and read my comments, which I hope are more entertaining even if they are less informative. The big features of Stata 16 are - Lasso, both for prediction and for inference Reproducible and automatically updating reports New meta-analysis suite Revamped and expanded choice modeling (margins works everywhere) Integration of Python with Stata Bayesian predictions, multiple chains, and more Extended regression models (ERMs) for panel data Importing or SAS and SPSS data sets Flexible nonparametric series regression Multiple data sets in memory, meaning frames Sample-size analysis for confidence intervals Nonlinear DSGE models Multiple-group IRT Panel data Heckman selection models NLMEs with lags: multiple-dose pharmacokinetic models and more Heteroskedastic ordered probit Graph sizes in inches, centimeters, and printer points Numerical integration in Mata Linear programming in Mata Do-file Editor: Autocompletion, syntax highlighting, and more Stata for Mac: Dark Mode and tabbed windows Set matsize obviated Oh, and in Stata / MP, Stata matrices can now be up to 65,534 x 65,534, meaning you can fit models with over 65,000 right-hand-side variables. Meanwhile, Mata matrices remain limited only by memory. That's it The highlights are 58% or what's new in Stata 16, measured by the number of text lines required to describe them. Here is a sampling or what else is new. ranksum has new option exact to specify that exact p-values ​​are computed for the Wilcoxon rank-sum test. New setting set of iterlog controls or estimation commands display iteration logs. menl has new option lrtest that reports a likelihood ratio test comparing the nonlinear mixed-effects model with the model fit by ordinary nonlinear regression. The bayes: prefix command now supports the new hetoprobit command so that you can fit Bayesian heteroskedastic ordered probits. The svy: prefix works with more estimation commands, namely, existing command hetoprobit and new commands cmmixlogit and cmxtmixlogit. New command export sasxport8 export datasets to SAS XPORT Version 8 Transport format. New command split sample splits data into random samples. It can create simple random samples, clustered samples, and balanced random samples. Balance splitting can be used for matched-treatment assignment.