I’m happy to announce the release of new BESH Stat version 0.21. The main new feature in this version is the Negative Binomial regression model. It fits the NB2 parametrization model. Results match those provided by R glm.nb function.
Category Archives: new version
BESH Stat version 0.18 released
I’m happy to announce the release of new BESH Stat version 0.18. The main change is the implementation of Zero-Inflated Poisson regression as described by Diane Lambert in [1]. BESH Stat results on default settings match on 4 decimal place to the R pscl package zeroinfl function parameter estimates and standard errors (by decreasing the …
BESH Stat version 0.17 released
I’m happy to announce the release of new BESH Stat version 0.17. The main change is the addition of exact p-value computation for the unordered R x C contingency tables (Fisher-Freeman-Halton Exact test). I was working on this on-and-off for several years now. The new procedure implements the network algorithm developed by Mehta and Patel …
New BESH Stat version 0.16 was released
New BESH Stat version 0.16 was released. This version was released earlier than planned, because I’ve identified a bug in Log-rank test that I introduced in the previous release during refactoring.
New BESH Stat version 0.15 was released
New BESH Stat version 0.15 was released. Main change is the addition of new Ordinal Logistic Regression procedure.
BESH Stat version 0.14 released
New BESH Stat version 0.14 was released. After quite some time when I was busy with other work I managed to extend the BESH stat Excel add-in capabilities by including the Poisson model into the add-in regression capabilities. So now it contains commonly used models in the area of clinical research (Ordinary least squares regression, …
BESH Stat version 0.13 released
New BESH Stat version 0.13 was released. This is mainly a maintenance release.
BESH Stat version 0.12 released
New BESH Stat version 0.12 was released.
BESH Stat version 0.11 released
New BESH Stat version 0.11 was released. The main new feature is the addition of the Multinomial logistic regression model.