person("Alexander", "Keil", , "alex.keil@nih.gov", role = c("aut", "cre"))

Quantile G-Computation Extensions for Effect Measure
Modification
G-computation for a set of time-fixed exposures
with quantile-based basis functions, possibly under linearity and
homogeneity assumptions. Effect measure modification in this method is a way
to assess how the effect of the mixture varies by a binary, categorical or continuous variable.
Reference: Alexander P. Keil, Jessie P.
Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
Alexandra J. White (2019) A quantile-based g-computation approach to
addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
qgcomp, arm, survival, future, future.apply, ggplot2,
gridExtra

Functions to estimate parameters and compute statistics and their corresponding confidence intervals (e.g. heritability, genetic correlations...). All functions are written for sib analysis with up to 2 levels of nesting. Parametric simulations allow the calculation of bootstrap confidence intervals and the evaluation of dependence on different assumptions.

The package qgen is a collection of functions to analyse quantitative genetic data. It is especially helpful to perform parametric resampling of quantitative genetic data sets. Resampling allows first to determine a priori the expected variance of an estimator, second for a given empirical data set to calculate bootstrap confidence intervals, and third to evaluate different estimators and confidence intervals.

Statistical Tools for Quantitative Genetic Analyses
c(person("Peter", "Soerensen", email = "peter.sorensen@r-qgg.org",
role = c("aut", "cre")),
person("Palle", "Duun Rohde", email = "palle.d.rohde@mbg.au.dk",
role = "aut"),
person("Izel", "Fourie Soerensen", email = "izel.sorensen@gmail.com",
role = "aut"))
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses.
Rohde et al. (2019) <doi:10.1101/503631>.
data.table, parallel, statmod, stats, MCMCpack, MASS
Peter Soerensen [aut, cre],
Palle Duun Rohde [aut],
Izel Fourie Soerensen [aut]