Package: KRMM 1.0

Laval Jacquin

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_1.0.tar.gz
KRMM_1.0.zip(r-4.7-any)KRMM_1.0.zip(r-4.6-any)KRMM_1.0.zip(r-4.5-any)
KRMM_1.0.tgz(r-4.6-any)KRMM_1.0.tgz(r-4.5-any)
KRMM_1.0.tar.gz(r-4.7-any)KRMM_1.0.tar.gz(r-4.6-any)
KRMM_1.0.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://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

On CRAN:

Conda:

archivedpackagesr-universe

2.10 score 5 stars 25 scripts 287 downloads 1 mentions 4 exports 6 dependencies

Last updated from:8b462d9a0b (on package/KRMM). Checks:7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develNOTE114
source / vignettesOK170
linux-releaseNOTE106
macos-releaseNOTE106
macos-oldrelNOTE84
windows-develNOTE82
windows-releaseNOTE76
windows-oldrelNOTE59
wasm-releaseOK95

Exports:EM_REML_MMKernel_Ridge_MMPredict_kernel_Ridge_MMTune_kernel_Ridge_MM

Dependencies:cvToolsDEoptimRkernlablatticeMASSrobustbase