Package: KRMM 1.1

KRMM: Kernel Ridge Mixed Model

Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.

Authors:Laval Jacquin [aut, cre]

KRMM_1.1.tar.gz
KRMM_1.1.zip(r-4.7-any)KRMM_1.1.zip(r-4.6-any)KRMM_1.1.zip(r-4.5-any)
KRMM_1.1.tgz(r-4.6-any)KRMM_1.1.tgz(r-4.5-any)
KRMM_1.1.tar.gz(r-4.7-any)KRMM_1.1.tar.gz(r-4.6-any)
KRMM_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
KRMM/json (API)

# Install 'KRMM' in R:
install.packages('KRMM', repos = c('https://ljacquin.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ljacquin/krmm/issues

On CRAN:

Conda:

blupgblupgenomic-predictionkernel-methodsmixed-modelsvariance-components-estimation

2.98 score 1 stars 19 scripts 230 downloads 1 mentions 4 exports 11 dependencies

Last updated from:cb010a23da. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develERROR127
source / vignettesOK158
linux-releaseERROR127
macos-releaseERROR118
macos-oldrelERROR137
windows-develERROR82
windows-releaseERROR76
windows-oldrelERROR81
wasm-releaseOK104

Exports:em_reml_mmkrmmpredict_krmmtune_krmm

Dependencies:codetoolscvToolsDEoptimRdoParallelforeachiteratorskernlablatticeMASSMatrixrobustbase