Three steps variable selection procedure based on random forests.
Initially developed to handle high dimensional data (for which number of
variables largely exceeds number of observations), the package is very
versatile and can treat most dimensions of data, for regression and
supervised classification problems. First step is dedicated to eliminate
irrelevant variables from the dataset. Second step aims to select all
variables related to the response for interpretation purpose. Third step
refines the selection by eliminating redundancy in the set of variables
selected by the second step, for prediction purpose.
Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015)
<https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.
doParallel, foreach, parallel, Rborist, randomForest, ranger,
rpart

Provides decomposition methods for the skewness or the variance of a variable (e.g., wage).
By breaking distribution moments into independent components, users can
analyze changes in distributions across time or between groups.

Provides decomposition methods for the skewness or variance of a variable (e.g., wage).
By breaking distribution moments into independent components, users can
analyze changes in distributions across time or between groups.

Interactive Document for Working with Variance Analysis
An interactive document on  the topic of variance analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://predanalyticssessions1.shinyapps.io/chisquareVarianceTest/>.

Software for Analysing and Visualising Animal Movement from
Acoustic Telemetry Detections
Ross G. Dwyer, Mathew E. Watts, Hamish A. Campbell & Craig E. Franklin
Designed to facilitate the assimilation, analysis and synthesis of animal location and movement data collected by the VEMCO suite of acoustic transmitters and receivers. As well as database and geographic information capabilities the principal feature of VTrack is the qualification and identification of ecologically relevant events from the acoustic detection and sensor data. This procedure condenses the acoustic detection database by orders of magnitude, greatly enhancing the synthesis of acoustic detection data.
R (>= 3.1.0), foreach(>= 1.2.0), parallel, doParallel