Provides tools for response surface analysis, using a comparative framework that identifies best-fitting solutions across 37 families of polynomials. Many of these tools are based upon and extend the 'RSA' package, by testing a larger scope of polynomials (+27 families), more diverse response surface probing techniques (+acceleration points), more plots (+line of congruence, +line of incongruence, both with extrema), and other useful functions for exporting results.

The 'SaTScan'(TM) <https://www.satscan.org> software uses spatial and 
	space-time scan statistics to detect and evaluate spatial and space-time clusters. 
	With the 'rsatscan' package, you can run the external 'SaTScan' software from 
	within R using R data formats. To successfully select appropriate parameter 
	settings within 'rsatscan', you must first learn 'SaTScan'.

Package to Handle R Requests from R Service Bus Applications with JSON Payloads in a generic way.
    The incoming request is encoded as a string (character vector of length one) containing 
    the JSON file passed through by the client.

Performs robust and sparse correlation matrix estimation. Robustness is achieved based on a simple robust pairwise correlation estimator, while sparsity is obtained based on thresholding. The optimal thresholding is tuned via cross-validation. See Serra, Coretto, Fratello and Tagliaferri (2018) <doi:10.1093/bioinformatics/btx642>.

A statistical tool for multivariate modeling and clustering using stepwise cluster analysis. The modeling output of rSCA is constructed as a cluster tree to represent the complicated relationships between multiple dependent and independent variables. A free tool (named rSCA Tree Generator) for visualizing the cluster tree from rSCA is also released and it can be downloaded at <https://rscatree.weebly.com/>.

As an advanced approach to computerized adaptive testing (CAT), 
  shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically 
  assembles entire shadow tests as a part of 
  selecting items throughout the testing process.
  Selecting items from shadow tests guarantees the compliance of all content 
  constraints defined by the blueprint. 'RSCAT' is an R package for the 
  shadow-test approach to CAT. The objective of 
  'RSCAT' is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation;
  2) Contributing to the academic and scientific community for CAT research.
  RSCAT is currently designed for dichotomous items based on the three-parameter logistic (3PL) model.