Incrementally Build Complex Plots using Natural Semantics (wheatmap)
Builds complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as ‘LeftOf’, ‘RightOf’, ‘TopOf’, and ‘Beneath’ and more. Other features include clustering, dendrograms and integration with ‘ggplot2’ generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.

Generalized Integration Model (gim)
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models.

Pathway Enrichment Analysis Utilizing Active Subnetworks (pathfindR)
Pathway enrichment analysis enables researchers to uncover mechanisms underlying the phenotype. pathfindR is a tool for pathway enrichment analysis utilizing active subnetworks. It identifies active subnetworks in a protein-protein interaction network using user-provided a list of genes. It performs pathway enrichment analyses on the identified subnetworks. pathfindR also offers functionality to cluster enriched pathways and identify representative pathways. The method is described in detail in Ulgen E, Ozisik O, Sezerman OU. 2018. pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks. bioRxiv. <doi:10.1101/272450>.

A ‘Java’ Platform Integration for ‘R’ with Programming Languages ‘Groovy’, ‘JavaScript’, ‘JRuby’ (‘Ruby’), ‘Jython’ (‘Python’), and ‘Kotlin’ (jsr223)
Provides a high-level integration for the ‘Java’ platform that makes ‘Java’ objects easy to use from within ‘R’; provides a unified interface to integrate ‘R’ with several programming languages; and features extensive data exchange between ‘R’ and ‘Java’. The ‘jsr223’-supported programming languages include ‘Groovy’, ‘JavaScript’, ‘JRuby’ (‘Ruby’), ‘Jython’ (‘Python’), and ‘Kotlin’. Any of these languages can use and extend ‘Java’ classes in natural syntax. Furthermore, solutions developed in any of the ‘jsr223’-supported languages are also accessible to ‘R’ developers. The ‘jsr223’ package also features callbacks, script compiling, and string interpolation. In all, ‘jsr223’ significantly extends the computing capabilities of the ‘R’ software environment.

Parsimonious Gaussian Mixture Models (pgmm)
Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>.

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