Parallelized Genomic Prediction and GWAS
Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP).
At its core, the package has a Gibbs Sampler that allows running univariate linear
mixed models that have both, sparse and dense design matrices. The parallel sampling method
in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for
a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models.
In addition, the package offers parallelized functions for common tasks like genome-wide
association studies and cross validation in a memory efficient way.

c(person(given = "Marvin N.",
family = "Wright",
role = c("aut", "cre"),
email = "cran@wrig.de",
comment = c(ORCID = "0000-0002-8542-6291")),
person(given = "David S.",
family = "Watson",
role = "aut"))
A general test for conditional independence in supervised learning
algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>.
Implements a conditional variable importance measure which can be applied to any supervised
learning algorithm and loss function. Provides statistical inference procedures without
parametric assumptions and applies equally well to continuous and categorical predictors
and outcomes.
BEST, mlr3learners, ranger, glmnet, testthat (>= 3.0.0),
knitr, rmarkdown, doParallel
Marvin N. Wright [aut, cre] (<https://orcid.org/0000-0002-8542-6291>),
David S. Watson [aut]

c(person(given =c("Oscar", "A."), family = "Linares", role =
c("aut", "cre"), email = "OALinaresMD@gmail.com"),
person(given =c( "David", "T."), family = "Daly", role = "aut",
email = "daviddalysr@att.net"))
The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc for target therapeutic concentration, (2) dr for dose rate, (3) di for dosing interval, (4) dm for maintenance dose, (5) bc.ttc for back calculation, (6) ar for accumulation ratio, (7) dpo for orally administered dose, (8) cmax for peak concentration, (9) css for steady-state concentration, and (10) cmin for trough. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135.
The authors take no responsibility for the outcome of
therapy in any patient in which the techniques described in
this package have been utilized.
Oscar A. Linares, MD <OALinaresMD@gmail.com>
Oscar A. Linares [aut, cre],
David T. Daly [aut]