c(
person("Anthony", "Ives", , "arives@wisc.edu", c("aut", "cre")),
person("Daijiang", "Li", , "daijianglee@gmail.com", role = "ctb"))
Three methods to calculate R2 for models with correlated errors,
including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed
Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in
Ives 2018 <doi:10.1093/sysbio/syy060>.
Anthony Ives [aut, cre],
Daijiang Li [ctb]

lme4, phylolm (>= 2.6.2), ape, utils, Matrix, nlme, phyr (>=
1.0.3)

Genomic selection and association analysis
Contains tools for genomics research: a maximum-likelihood
algorithm for ridge regression-BLUP (RR.BLUP), a function for
genome-wide association analysis (GWA), and a genomic selection
algorithm that combines ridge regression and subset selection
(RR.BLUP.MR).

Ridge Regression-BLUP and related methods for genomic selection
At the core of this package is the function mixed.solve,
which contains a fast ML/REML algorithm for mixed models with
one variance component (other than the residual error).  This
algorithm can be used for association analysis (function GWA)
and genomic prediction (function kinship.BLUP), for which
several kinship models are available (e.g., realized
relationship and Gaussian).

Ridge regression-BLUP and related methods for genomic selection
One application is to estimate marker effects by ridge
regression; alternatively, BLUPs can be calculated based on
kinship to a training population.  Kinship options include the
realized relationship matrix, Gaussian and exponential spatial
models.

Ridge Regression and kernel methods for genomic selection
One application is to estimate marker effects by ridge
regression; alternatively, BLUPs can be calculated based on
kinship.  The genetic correlation between lines can be modeled
using a marker-based, additive relationship matrix, or via
Gaussian and exponential kernel models.

Ridge regression and other kernels for genomic selection
One application is to estimate marker effects by ridge
regression; alternatively, BLUPs can be calculated based on
kinship.  The genetic correlation between lines can be modeled
using a marker-based, additive relationship matrix, or via
Gaussian and exponential kernels.