Provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.

Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.

Analyzes gene expression
  (time series) data with focus on the inference of gene networks.
  In particular, GeneNet implements the methods of Schaefer and 
  Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007)
  for learning large-scale gene association networks (including
  assignment of putative directions).

A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures.

Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains functions to calculate haplotype specific odds ratio and the power of two stage design for GWAS studies.

Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.

Function gmcmtx0() computes a more reliable (general) 
    correlation matrix. Since causal paths from data are important for all sciences, the
    package provides many sophisticated functions. causeSummBlk() and causeSum2Blk()
    give easy-to-interpret causal paths.  Let Z denote control variables and compare 
    two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 
    says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent"
    than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These
    inequalities between many absolute values are quantified by four orders of 
    stochastic dominance. Our third criterion Cr3, for the causal path X to Y,
    requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|.
    The function parcorVec() reports generalized partials between the first
    variable and all others.  The package provides several R functions including
    get0outliers() for outlier detection, bigfp() for numerical integration by the
    trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
    canonRho() for generalized canonical correlations, depMeas() measures nonlinear
    dependence, and causeSummary(mtx) reports summary of causal paths among matrix 
    columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx()
    can rank several stocks. Functions whose names begin with 'boot' provide bootstrap
    statistical inference, including a new bootGcRsq() test for "Granger-causality" 
    allowing nonlinear relations. A new tool for evaluation of out-of-sample
    portfolio performance is outOFsamp(). Panel data implementation is now included.
    See eight vignettes of the package for theory, examples, and
    usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.