Package: icmm 1.2

icmm: Empirical Bayes Variable Selection via ICM/M Algorithm

Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.

Authors:Vitara Pungpapong [aut, cre], Min Zhang [ctb], Dabao Zhang [ctb]

icmm_1.2.tar.gz
icmm_1.2.zip(r-4.7)icmm_1.2.zip(r-4.6)icmm_1.2.zip(r-4.5)
icmm_1.2.tgz(r-4.6-any)icmm_1.2.tgz(r-4.5-any)
icmm_1.2.tar.gz(r-4.7-any)icmm_1.2.tar.gz(r-4.6-any)
icmm_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
icmm/json (API)

# Install 'icmm' in R:

install.packages('icmm', repos = c('https://vitara-p.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

initbetaBinomial - Initial values for the regression coefficients used in example for running ICM/M algorithm in binary logistic model
initbetaCox - Initial values for the regression coefficients used in example for running ICM/M algorithm in Cox's model
initbetaGaussian - Initial values for the regression coefficients used in example for running ICM/M algorithm in normal linear regression model
linearrelation - Linear structure of predictors
simBinomial - Simulated data from the binary logistic regression model
simCox - Simulated data from Cox's regression model
simGaussian - Simulated data from the normal linear regression model

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.28 score 19 scripts 198 downloads 12 exports 3 dependencies

Last updated from:f9f25daed5. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	100
source / vignettes	OK	138
linux-release-x86_64	OK	97
macos-release-arm64	OK	111
macos-oldrel-arm64	OK	165
windows-devel	OK	71
windows-release	OK	57
windows-oldrel	OK	68
wasm-release	OK	107

Exports:get.ab get.alpha get.beta get.beta.ising get.pseudodata.binomial get.pseudodata.cox get.sigma get.wpost get.wprior get.zeta get.zeta.ising icmm

Dependencies:EbayesThresh MASS wavethresh

Citation

Readme and manuals

Help Manual

Help page	Topics
Empirical Bayes Variable Selection via ICM/M	icmm-package
Hyperparameter estimation for 'a' and 'b'.	get.ab
Hyperparameter estimation for 'alpha'.	get.alpha
Obtain model coefficient without assuming prior on structure of predictors.	get.beta
Obtain a regression coefficient when assuming Ising prior (with structured predictors).	get.beta.ising
Obtain pseudodata based on the binary logistic regression model.	get.pseudodata.binomial
Obtain pseudodata based on the Cox's regression model.	get.pseudodata.cox
Standard deviation estimation.	get.sigma
Estimate posterior probability of mixing weight.	get.wpost
Mixing weight estimation.	get.wprior
Local posterior probability estimation	get.zeta
Local posterior probability estimation.	get.zeta.ising
Empirical Bayes Variable Selection	icmm
Initial values for the regression coefficients used in example for running ICM/M algorithm in binary logistic model	initbetaBinomial
Initial values for the regression coefficients used in example for running ICM/M algorithm in Cox's model	initbetaCox
Initial values for the regression coefficients used in example for running ICM/M algorithm in normal linear regression model	initbetaGaussian
Linear structure of predictors	linearrelation
Simulated data from the binary logistic regression model	simBinomial
Simulated data from Cox's regression model	simCox
Simulated data from the normal linear regression model	simGaussian

Package: icmm 1.2

icmm: Empirical Bayes Variable Selection via ICM/M Algorithm

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)