Package: emBayes 0.1.6
emBayes: Robust Bayesian Variable Selection via Expectation-Maximization
Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'.
Authors:
emBayes_0.1.6.tar.gz
emBayes_0.1.6.tar.gz(r-4.5-noble)emBayes_0.1.6.tar.gz(r-4.4-noble)
emBayes_0.1.6.tgz(r-4.4-emscripten)emBayes_0.1.6.tgz(r-4.3-emscripten)
emBayes.pdf |emBayes.html✨
emBayes/json (API)
# Install 'emBayes' in R: |
install.packages('emBayes', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- data - Simulated gene expression example data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 months agofrom:98ef0812da. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 14 2024 |
R-4.5-linux-x86_64 | OK | Nov 14 2024 |
Exports:cv.emBayesemBayes
Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppArmadilloRcppEigenshapesurvival
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Robust Bayesian Variable Selection via Expectation-Maximization | emBayes-package |
k-folds cross-validation for 'emBayes' | cv.emBayes |
simulated gene expression example data | data |
fit a model with given tuning parameters | emBayes |
print an cv.emBayes result | print.cv.emBayes |
print an emBayes result | print.emBayes |