R/gap.R

In gap: Genetic Analysis Package

#' Genetic analysis package
#'
#' As is first reported, it is designed as an integrated package for genetic data
#' analysis of both population and family data. Currently, it contains functions for
#' sample size calculations of both population-based and family-based designs, probability
#' of familial disease aggregation, kinship calculation, statistics in linkage analysis,
#' and association analysis involving genetic markers including haplotype analysis with or
#' without environmental covariates. Over years, the package has been developed in-between
#' many projects hence also in line with the name (gap).
#'
#' @details
#' We have incorporated functions for a wide range of problems as shown below.
#'
#' \tabular{ll}{
#' \cr
#' \strong{ANALYSIS}\cr
#' \cr
#' ACDE              \tab      AE/ACE/ADE models using nuclear families\cr
#' AE3               \tab      AE model using nuclear family trios\cr
#' bt                \tab      Bradley-Terry model for contingency table\cr
#' ccsize            \tab      Power and sample size for case-cohort design\cr
#' cs                \tab      Credibel set\cr
#' fbsize            \tab      Sample size for family-based linkage and association design\cr
#' gc.em             \tab      Gene counting for haplotype analysis\cr
#' gcontrol          \tab      genomic control\cr
#' gcontrol2         \tab      genomic control based on p values\cr
#' gcp               \tab      Permutation tests using GENECOUNTING\cr
#' gc.lambda         \tab      Estimation of the genomic control inflation statistic (lambda)\cr
#' genecounting      \tab      Gene counting for haplotype analysis\cr
#' gif               \tab      Kinship coefficient and genetic index of familiality\cr
#' hap               \tab      Haplotype reconstruction\cr
#' hap.em            \tab      Gene counting for haplotype analysis\cr
#' hap.score         \tab      Score statistics for association of traits with haplotypes\cr
#' htr               \tab      Haplotype trend regression\cr
#' h2.jags           \tab      Heritability estimation based on genomic relationship matrix using JAGS\cr
#' hwe               \tab      Hardy-Weinberg equilibrium test for a multiallelic marker\cr
#' hwe.cc            \tab      A likelihood ratio test of population Hardy-Weinberg equilibrium \cr
#' hwe.hardy         \tab      Hardy-Weinberg equilibrium test using MCMC\cr
#' hwe.jags          \tab      Hardy-Weinberg equlibrium test for a multiallelic marker using JAGS\cr
#' invnormal         \tab      inverse Normal transformation\cr
#' kin.morgan        \tab      kinship matrix for simple pedigree\cr
#' LD22              \tab      LD statistics for two diallelic markers\cr
#' LDkl              \tab      LD statistics for two multiallelic markers\cr
#' lambda1000        \tab      A standardized estimate of the genomic inflation scaling to\cr
#'                   \tab      a study of 1,000 cases and 1,000 controls\cr
#' log10p            \tab      log10(p) for a standard normal deviate\cr
#' log10pvalue       \tab      log10(p) for a P value including its scientific format\cr
#' logp              \tab      log(p) for a normal deviate\cr
#' masize            \tab      Sample size calculation for mediation analysis\cr
#' MCMCgrm           \tab	    Mixed modeling with genetic relationship matrices\cr
#' mia               \tab      multiple imputation analysis for hap\cr
#' mr                \tab      Mendelian randomization analysis\cr
#' mtdt              \tab      Transmission/disequilibrium test of a multiallelic marker\cr
#' mtdt2             \tab      Transmission/disequilibrium test of a multiallelic marker\cr
#'                   \tab      by Bradley-Terry model\cr
#' mvmeta            \tab      Multivariate meta-analysis based on generalized least squares\cr
#' pbsize            \tab      Power for population-based association design\cr
#' pbsize2           \tab      Power for case-control association design\cr
#' pfc               \tab      Probability of familial clustering of disease\cr
#' pfc.sim           \tab      Probability of familial clustering of disease\cr
#' pgc               \tab      Preparing weight for GENECOUNTING\cr
#' print.hap.score   \tab      Print a hap.score object\cr
#' s2k               \tab      Statistics for 2 by K table\cr
#' sentinels         \tab      Sentinel identification from GWAS summary statistics\cr
#' tscc              \tab      Power calculation for two-stage case-control design\cr
#' \cr
#' \strong{GRAPHICS}\cr
#' \cr
#' asplot            \tab      Regional association plot\cr
#' ESplot            \tab      Effect-size plot\cr
#' circos.cis.vs.trans.plot \tab circos plot of cis/trans classification\cr
#' circos.cnvplot    \tab      circos plot of CNVs\cr
#' circos.mhtplot    \tab      circos Manhattan plot with gene annotation\cr
#' circos.mhtplot2   \tab      Another circos Manhattan plot\cr
#' cnvplot           \tab      genomewide plot of CNVs\cr
#' labelManhattan    \tab      Annotate Manhattan or Miami Plot\cr
#' makeRLEplot       \tab      make relative log expression plot\cr
#' METAL_forestplot  \tab      forest plot as R/meta's forest for METAL outputs\cr
#' mhtplot           \tab      Manhattan plot\cr
#' mhtplot2          \tab      Manhattan plot with annotations\cr
#' mhtplot.trunc     \tab      truncated Manhattan plot\cr
#' miamiplot         \tab      Miami plot\cr
#' miamiplot2        \tab      Miami plot\cr
#' mr_forestplot     \tab      Mendelian Randomization forest plot\cr
#' pedtodot          \tab      Converting pedigree(s) to dot file(s)\cr
#' pedtodot_verbatim \tab      Pedigree-drawing with graphviz\cr
#' plot.hap.score    \tab      Plot haplotype frequencies versus haplotype score statistics\cr
#' qqfun             \tab      Quantile-comparison plots\cr
#' qqunif            \tab      Q-Q plot for uniformly distributed random variable\cr
#' qtl2dplot         \tab      2D QTL plot\cr
#' qtl2dplotly       \tab      2D QTL plotly\cr
#' qtl3dplotly       \tab      3D QTL plotly\cr
#' \cr
#' \strong{UTITLITIES}\cr
#' \cr
#' SNP               \tab      Functions for single nucleotide polymorphisms (SNPs)\cr
#' BFDP              \tab      Bayesian false-discovery probability\cr
#' FPRP              \tab      False-positive report probability\cr
#' ab                \tab      Test/Power calculation for mediating effect\cr
#' b2r               \tab      Obtain correlation coefficients and their variance-covariances\cr
#' chow.test         \tab      Chow's test for heterogeneity in two regressions\cr
#' chr_pos_a1_a2     \tab      Form SNPID from chromosome, posistion and alleles\cr
#' ci2ms             \tab      Effect size and standard error from confidence interval\cr
#' cis.vs.trans.classification \tab a cis/trans classifier\cr
#' comp.score        \tab      score statistics for testing genetic linkage of quantitative trait\cr
#' GRM functions     \tab      ReadGRM, ReadGRMBin, ReadGRMPLINK, ReadGRMPCA, WriteGRM,\cr
#'                   \tab      WriteGRMBin, WriteGRMSAS\cr
#'                   \tab      handle genomic relationship matrix involving other software\cr
#' get_b_se          \tab      Get b and se from AF, n, and z\cr
#' get_pve_se        \tab      Get pve and its standard error from n, z\cr
#' get_sdy           \tab      Get sd(y) from AF, n, b, se\cr
#' h2G               \tab      A utility function for heritability\cr
#' h2GE              \tab      A utility function for heritability involving gene-environment interaction\cr
#' h2l               \tab      A utility function for converting observed heritability to its counterpart\cr
#'                   \tab      under liability threshold model\cr
#' h2_mzdz           \tab      Heritability estimation according to twin correlations\cr
#' klem              \tab      Haplotype frequency estimation based on a genotype table\cr
#'                   \tab      of two multiallelic markers\cr
#' makeped           \tab      A function to prepare pedigrees in post-MAKEPED format\cr
#' metap             \tab      Meta-analysis of p values\cr
#' metareg           \tab      Fixed and random effects model for meta-analysis\cr
#' muvar             \tab      Means and variances under 1- and 2- locus (diallelic) QTL model\cr
#' qtlClassifier     \tab      A QTL cis/trans classifier\cr
#' qtlFinder         \tab      Distance-based signal identification\cr
#' read.ms.output    \tab      A utility function to read ms output\cr
#' revStrand         \tab      Allele on the reverse strand\cr
#' runshinygap       \tab      Start shinygap\cr
#' snptest_sample    \tab      A utility to generate SNPTEST sample file\cr
#' whscore           \tab      Whittemore-Halpern scores for allele-sharing\cr
#' weighted.median   \tab      Weighted median with interpolation\cr
#' \cr
#' }
#'
#' @section Usage:
#' Vignettes on package usage:
#' - Genetic Analysis Package. `vignette("gap")`.
#' - Shiny for Genetic Analysis Package (gap) Designs. `vignette("shinygap")`.
#' - JSS paper: Genetic Analysis Package. `vignette("jss")`.
#'
#' @docType package
#' @name gap
#' @aliases gap-package
#'
#' @import dplyr gap.datasets
#' @importFrom grDevices dev.off palette pdf xy.coords
#' @importFrom graphics abline arrows axis box boxplot identify legend lines mtext par points segments text title rect strwidth strheight
#' @importFrom stats as.formula coef coefficients cor complete.cases dnorm glm integrate lm logLik median nlm pchisq pf pnorm ppoints pt qchisq qnorm qqplot qt quantile rbinom rexp rmultinom rnorm runif sd setNames var
#' @importFrom utils data head packageDescription read.csv read.delim read.table tail write.table globalVariables
#' @importFrom Rdpack reprompt
#' @useDynLib gap
#'
#' @author Jing Hua Zhao in collaboration with other colleagues and with
#'         help from Kurt Hornik, Brian Ripley, Uwe Ligges and Achim Zeileis
#'
#' maitained by Jing Hua Zhao <jinghuazhao@hotmail.com>
#'
#' @references
#' \insertRef{zhao07}{gap}
#'
#' @keywords internal

"_PACKAGE"