person(given = "Jonathan", family = "Chassot", email = "jonathanlaurentdominique.chassot@ebg.admin.ch",

Title: Identifying Interactions Between Binary Predictors
Description: A global statistical learning method
which tries to find the best set of predictors and interactions
between predictors for modeling binary or quantitative response data.
Several search algorithms and ensembling techniques are implemented
allowing for finetuning the method to the specific problem.
Interactions with single quantitative covariables can be properly
taken into account by also splitting after those or by fitting

Title: Combined Graphs for Logistic Regression
Description: Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions.
Packaged: 2017-11-16 20:29:41 UTC; marcelino

Title: Analysis of sparse contingency tables with penalization approaches
Description: Analysis of sparse contingency tables with penalization approaches
Packaged: Tue Nov 13 15:24:15 2007; dahinden

Title: Firth's bias reduced logistic regression
Author: Meinhard Ploner <meinhard.ploner@akh-wien.ac.at>, Daniela Dunkler, i
Description: Firth's bias reduced logistic regression approach with
penalized profile likelihood based confidence intervals for

Author: Meinhard Ploner, Daniela Dunkler,
Harry Southworth, Georg Heinze <georg.heinze@meduniwien.ac.at>

Author: Meinhard Ploner, Daniela Dunkler, Harry Southworth, Georg
Packaged: Tue Apr  5 08:48:48 2005; Administrator

Packaged: Thu Nov 17 10:41:36 2005; Administrator

Packaged: Thu Apr 20 16:48:54 2006; Administrator

Author: Georg Heinze <georg.heinze@meduniwien.ac.at>, Meinhard Ploner,
Daniela Dunkler (former versions), Harry Southworth (former

Title: Firth's Bias-Reduced Logistic Regression
Description: Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys
prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal
solution to the problem of separation in logistic regression. If needed, the bias reduction can be turned off such that ordinary
maximum likelihood logistic regression is obtained.
Packaged: 2016-12-19 15:27:22 UTC; Biometrikus