Tools for Mapping of Quantitative Traits of Genetically Related
Individuals and Calculating Identity Coefficients from
Pedigrees

person("Riyan", "Cheng", role = c("aut","cre"), email = "riyancheng@hotmail.com")

This software provides tools for quantitative trait
mapping in populations such as advanced intercross lines where
relatedness among individuals should not be ignored. It can
estimate background genetic variance components, impute missing
genotypes, simulate genotypes, perform a genome scan for
putative quantitative trait loci (QTL), and plot mapping
results. It also has functions to calculate identity
coefficients from pedigrees, especially suitable for pedigrees
that consist of a large number of generations, or estimate
genetic matrices from pedigrees and from genotypic data in
certain circumstances.

gdata, graphics, grDevices, lattice, stats

Quantile-Optimal Treatment Regimes with Censored Data
c(person("Yu", "Zhou", , "zhou0269@umn.edu", role = c("cre", "aut")),
person("Lan", "Wang", , "wangx346@umn.edu  ", role = "ctb"))
Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data.
Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.
survival, rgenoud (>= 5.8), quantreg (>= 5.18), stats,
grDevices, methods, Rdpack, MatrixModels
parallel, stringr, testthat, faraway, quantoptr (>= 0.1.3),
survminer

Partitioning Uncertainty Components of an Incomplete Ensemble of
Climate Projections
MASS, expm, Rfast, foreach, doParallel, methods, stats,
graphics, grDevices
These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates.
- Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.