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KRMM  

Kernel Ridge Mixed Model
View on CRAN: Click here


Download and install KRMM package within the R console
Install from CRAN:
install.packages("KRMM")

Install from Github:
library("remotes")
install_github("cran/KRMM")

Install by package version:
library("remotes")
install_version("KRMM", "1.0")



Attach the package and use:
library("KRMM")
Maintained by
Laval Jacquin
[Scholar Profile | Author Map]
First Published: 2017-06-03
Latest Update: 2017-06-03
Description:
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. .
How to cite:
Laval Jacquin (2017). KRMM: Kernel Ridge Mixed Model. R package version 1.0, https://cran.r-project.org/web/packages/KRMM. Accessed 11 May. 2025.
Previous versions and publish date:
No previous versions
Other packages that cited KRMM R package
View KRMM citation profile
Other R packages that KRMM depends, imports, suggests or enhances
Complete documentation for KRMM
Functions, R codes and Examples using the KRMM R package
Some associated functions: EM_REML_MM . KRMM-package . Kernel_Ridge_MM . Predict_kernel_Ridge_MM . Tune_kernel_Ridge_MM . 
Some associated R codes: EM_REML_MM.R . Kernel_Ridge_MM.R . Predict_kernel_Ridge_MM.R . Tune_kernel_Ridge_MM.R .  Full KRMM package functions and examples
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