The following free r packages , r projects , r code , and r examples are used for Fit and test metaregression models. This package fits meta regression models and generates a
number of statistics: next to t- and z-tests, the likelihood ratio, bartlett corrected likelihood ratio and permutation tests are performed on the model coefficients.
The following free r packages , r projects , r code , and r examples are used for A max-margin supervised Sparse Topical Coding Model. This is a C++ implementation of Sparse Topical Coding (STC), a model of discrete data which is fully described in Zhu et al.
The following free r packages , r projects , r code , and r examples are used for Structural Equation Modeling Using Partial Least Squares. Fits structural equation models using partial least squares (PLS). The PLS approach is referred to as 'soft-modeling' technique requiring no distributional assumptions on the observed data.
The following source code and examples are used for Bias-corrected Bayesian Classification with Selected Features that predict the discrete class labels based on a selected subset of high-dimensional features, such as expression levels of genes.
The following source code and examples are used for Multifactor Dimensionality Reduction Analysis that provides various approaches to handling missing values for the MDR analysis to identify gene-gene interactions using biallelic marker data in genetic association studies.
The following source code and examples are used for Bayesian Sampling for Stick-breaking Mixtures that implement sampling algorithms for a variety of Bayesian stick-breaking (marginally DP) mixture models.
The following source code and examples are about Bayesian Methods for Identifying the Most Harmful Medication Errors that implements two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm.
The following package and source code is used for weighted SVM methods with penalization form.it can be used to build up weighted SVM easily and examine classification algorithm properties under weighted SVM
Graphical Markov models (GMM) use graphs, either undirected, directed, or mixed, to represent multivariate dependences in a visual and computationally efficient manner. The following are Functions for analyzing and fitting Graphical Markov models.
The following package is about Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ...