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sampleSelection 1.0 released
The recently released version of the sampleSelection package has the version number 1.0, because we consider the package now to be mature and feature-complete. Compared to version 0.6 of the sampleSelection package (which is described in an article in the Journal of Statistical Software [1]), the new version has, for instance, following new features:
Tutorial "Using R for Agricultural Economics Research"
Tutorial "Using R for Agricultural Economics Research" at the Congress of the European Association of Agricultural Economists (EAAE) in Ljubljana (Slovenia) on August 26, 2014.
Package 'stargazer' supports sampleSelection models
The R package 'stargazer' creates LaTeX code and ASCII text for well-formatted regression tables. It supports the most commonly used regression functions such as lm(), or glm() and its latest version (4.5) also supports objects from the binaryChoice(), heckit() and selection() functions :-) The 'stargazer' package is available on CRAN:
mvProbit used in research paper
We published the working paper, which was the reason for me to develop the R package "mvProbit":
sampleSelection 0.7-0 released
This version includes a major extension, binary outcome selection models. It should still be considered as experimental.
mvProbit released on CRAN
I am happy to announce the initial release of the "mvProbit" package on CRAN (version 0.1-0). This package provides tools for econometric analysis with Multivariate Probit Models. While these models can be estimated also by several other statistical software packages (e.g. LIMDEP/NLOGIT, STATA), "mvProbit" is much more flexible and powerful in calculating marginal effects. To my best knowledge, "mvProbit" is the only statistical software package that can calculate various marginal effects including their standard errors at arbitrary user-defined values of the explanatory (and dependent) variables (e.g. at all observations or at the sample mean): marginal effects on the unconditional expectations of the dependent variables and marginal effects on the conditional expectations of each dependent variable at all possible combinations of the other dependent variables. Feedback is very welcome!
