This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data.

This package provides a thin wrapper around the ajv JSON validation package for JavaScript. See <http://epoberezkin.github.io/ajv/> for details.

This package performs analysis of categorical-variable with missing values. Implements methods from Schafer, JL, Analysis of Incomplete Multivariate Data, Chapman and Hall.

Estimate the severity of a disease and ascertainment of cases, as discussed in Nishiura et al. (2009) <doi:10.1371/journal.pone.0006852>.

C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).

Light weight implementation of the standard distribution functions for the chi distribution, wrapping those for the chi-squared distribution in the stats package.

Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.

This package provides functions to extract joint planes from 3D triangular mesh derived from point cloud and makes data available for structural analysis.

Set of common functions used for manipulating colors, detecting and interacting with RStudio', modeling, formatting, determining users operating system, feature scaling, and more!

This package implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.

This package provides a general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

This package performs bivariate composite likelihood and full information maximum likelihood estimation for polytomous logit-normit (graded logistic) item response theory (IRT) models.

Analysis of crossover interference in experimental crosses, particularly regarding the gamma model. See, for example, Broman and Weber (2000) <doi:10.1086/302923>.

This is a package for computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization.

This package provides cover-tree and kd-tree fast k-nearest neighbor search algorithms. Related applications including KNN classification, regression and information measures are implemented.

This package supports the analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003).

This package provides tools for Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).

The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach.

Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).

An iterative algorithm that improves the proximity matrix (PM) from a random forest (RF) and the resulting clusters as measured by the silhouette score.

Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data: ICC, Finn-Coefficient, Robinson's A, Kendall's W, Cohen's Kappa, ...

This package contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).

The Retained Component Criterion for Principal Component Analysis (RCC_PCA) is a tool to determine the optimal number of components to retain in PCA.

Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).