MLPA analysis to detect gains and loses in genes
MLPAstats is a software designed to test copy number
differences between case and control samples, identified with
multiplex-dependent probe amplification (MLPA) data. The
package incorporates several methods for data normalization and
copy number alterations such as REX-regression, threshold
approach and mixed-models.

Multi-Label Prediction Using Gibbs Sampling (and Classifier
Chains)
person("Mikhail", "Popov", email = "mikhail@mpopov.com",
role = c("aut", "cre"), comment = "@bearloga on Twitter")
An implementation of classifier chains (CC's) for multi-label
prediction. Users can employ an external package (e.g. 'randomForest',
'C50'), or supply their own. The package can train a single set of CC's or
train an ensemble of CC's -- in parallel if running in a multi-core
environment. New observations are classified using a Gibbs sampler since
each unobserved label is conditioned on the others. The package includes
methods for evaluating the predictions for accuracy and aggregating across
iterations and models to produce binary or probabilistic classifications.
Mikhail Popov [aut, cre] (@bearloga on Twitter)

A package for Multilinear Regression Model Population Analysis
This package provides Multilinear Regression Model Population
Analysis to build a pool of models between quantitative activity and chemical
descriptors. Also some useful model validation functions. Contains all
molecular descriptors of 101 organic compounds and acticity dataset.

Interactive Application for Working with Multiple Linear
Regression
An interactive application for working with multiple linear regression technique. The application has a template for solving problems on multiple linear regression. Runtime examples are provided in the package function as well as at  <https://kartikeyastat.shinyapps.io/MLR_WEB_K/>.

An interactive application for working with multiple linear regression technique. The application has a template for solving problems on multiple linear regression. Runtime examples are provided at  <https://kartikeyastat.shinyapps.io/MLR_WEB_K/>.