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krm  

Kernel Based Regression Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("krm", "2022.10-17")



Attach the package and use:
library("krm")
Maintained by
Youyi Fong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2013-11-03
Latest Update: 2022-10-18
Description:
Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) .
How to cite:
Youyi Fong (2013). krm: Kernel Based Regression Models. R package version 2022.10-17, https://cran.r-project.org/web/packages/krm. Accessed 04 Jun. 2026.
Previous versions and publish date:
13.10-31 (2013-11-03 18:14), 13.11-03 (2013-11-05 08:57), 2014.12-1 (2014-12-02 07:45), 2015.3-4 (2015-03-04 21:30), 2016.7-9 (2016-07-10 11:55), 2018.1-24 (2018-01-24 19:02), 2018.8-17 (2018-08-18 06:30), 2019.12-11 (2019-12-10 19:50), 2020.5-20 (2020-05-21 14:50)
Other packages that cited krm R package
View krm citation profile
Other R packages that krm depends, imports, suggests or enhances
Complete documentation for krm
Functions, R codes and Examples using the krm R package
Some associated functions: aa.prop.list . chi.norm . cloud9 . dirichlet_functions . getSeqKernel . krm.most . krm . krm.score.test . profile_HMM . seq_alignment . seq_read_write . sim.liu . 
Some associated R codes: EQQ.R . dirichlet_functions.R . krm.most.R . krm.score.test.R . misc.R . profile_HMM.R . seq_alignment.R . seq_read_write.R . sim.liu.R .  Full krm package functions and examples
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