|Address:||Department of Applied Statistics|
Johannes Kepler Universität Linz
Altenbergerstraße 69, 4040 Linz, Austria
- flexmix: Flexible Mixture
A general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models, and model-based clustering.
Provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan.
- bayesmix Bayesian Mixture Models with JAGS
Mining Association Rules and Frequent Itemsets
Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides interfaces to C implementations of the association mining algorithms Apriori and Eclat by C. Borgelt.
Automatic Generation of Simple (Statistical) Exams
Sweave-based automatic generation of exams with multiple-choice questions and arithmetic problems.
Mixtures of von Mises-Fisher Distributions
Provides functionality to fit and simulate mixtures of von Mises-Fisher distributions.
- betareg: Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions. In addition to maximum likelihood regression (for both mean and precision of a beta-distributed response), bias-corrected and bias-reduced estimation as well as finite mixture models and recursive partitioning for beta regressions are provided.