Item response theory based methods are used to compute
linking constants and conduct chain linking of unidimensional
or multidimensional tests for multiple groups under a common
item design.  The unidimensional methods include the Mean/Mean,
Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous
(1PL, 2PL and 3PL) and/or polytomous (graded response, partial
credit/generalized partial credit, nominal, and multiple-choice
model) items.  The multidimensional methods include the least
squares method and extensions of the Haebara and Stocking-Lord
method using single or multiple dilation parameters for
multidimensional extensions of all the unidimensional
dichotomous and polytomous item response models.  The package
also includes functions for importing item and/or ability
parameters from common IRT software, conducting IRT true score
and observed score equating, and plotting item response
curves/surfaces, vector plots, information plots, and comparison
plots for examining parameter drift.

person("Xiaoran", "Tong", email = "tongxia1@msu.edu", role = c("aut", "cre"))
Provide function that reads binary genotype produced by 'PLINK' <https://www.cog-genomics.org/plink/1.9/input#bed> into a R matrix, or scan the genotype one variant at a time like apply(), it also provides functions that reads and writes genotype relatedness/kinship matrices created by 'PLINK' <https://www.cog-genomics.org/plink/1.9/distance#make_rel> or 'GCTA' <https://cnsgenomics.com/software/gcta/#MakingaGRM>. Currently it does not support writing back into 'PLINK' binary, it is best used for bringing data produced by 'PLINK' and 'GCTA' into R environment.

person("Hannah", "Meyer", email = "hannah@ebi.ac.uk",
role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4564-0899"))
Genotyping arrays enable the direct measurement of an individuals
genotype at thousands of markers. 'plinkQC' facilitates genotype quality
control for genetic association studies as described by Anderson and
colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic
statistics (e.g. missing genotyping rates per individual, allele frequencies
per genetic marker) and relationship functions accessible from 'R' and
generates a per-individual and per-marker quality control report.
Individuals and markers that fail the quality control can subsequently be
removed to generate a new, clean dataset. Removal of individuals based on
relationship status is optimised to retain as many individuals as possible
in the study.
methods, optparse, data.table (>= 1.11.0), R.utils, ggplot2,
ggforce, ggrepel, cowplot, UpSetR, dplyr