VarSelLCM - Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.

Last updated 6 years ago

openblascpp

4.95 score 2 dependents 49 scripts 372 downloads

MvBinary - Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution

Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.

Last updated 8 years ago

1.00 score 5 scripts 177 downloads

MHTrajectoryR - Bayesian Model Selection in Logistic Regression for the Detection of Adverse Drug Reactions

Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.

Last updated 9 years ago

1.00 score 5 scripts 135 downloads