Variable Selection for Optimal Individualized Dynamic Treatment Regime (ITRSelect)
Sequential advantage selection (SAS, Fan, Lu and Song, 2016) <arXiv:1405.5239> and penalized A-learning (PAL, Shi, et al., 2018) methods are implement for selecting important variables involved in optimal individualized (dynamic) treatment regime in both single-stage or multi-stage studies.

Mixture Gaussian Graphical Models (GGMM)
The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. For many real problems, the data are heterogeneous, which may contain some subgroups or come from different resources. This package provide a Gaussian Graphical Mixture Model (GGMM) for the heterogeneous data. You can refer to Jia, B. and Liang, F. (2018) at <arXiv:1805.02547> for detail.

Classes and Methods for ‘GRANBase’ (GRANCore)
Provides the classes and methods for GRANRepository objects that are used within the ‘GRAN’ build framework for R packages. This is primarily used by the ‘GRANBase’ package and repositories that are created by it.

Keen and Reliable Interface Subroutines for Bioinformatic Analysis (KRIS)
Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.

One-Sided Multinomial Probabilities (pmultinom)
Implements multinomial CDF (P(N1<=n1, …, Nk<=nk)) and tail probabilities (P(N1>n1, …, Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, …, lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.