Depends: R (>= 3.5.0), ROCR, methods, BEDMatrix, data.table

Description: This package performs an exact bayesian segmentation on
data and returns the probabilities of breakpoints, an ICL

Description: This package performs an exact bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc

Description: This package performs an exact Bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc

Description: Performs an exact Bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc.

Title: Empirical Bayesian Lasso (EBlasso) and Elastic Net (EBEN)
Description: Empirical Bayesian Lasso and Elastic Net algorithms are variable selection and estimation methods that outperform other state-of-the-art methods such as Lasso and Elastic Net methods.

Title: Empirical Bayesian Lasso and Elastic Net Methods for Generalized
Description: Empirical Bayesian Lasso and Elastic Net algorithms are variable selection and effect estimation methods that outperform other popular methods such as Lasso and Elastic Net methods.

Description: Provides Empirical Bayesian Lasso and Elastic Net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significant test for nonzero effects. This package outperforms other popular methods such as Lasso and Elastic Net methods in terms of Power of Detection, False Discovery Rate, and Power of Detection Grouping Effects.

Description: Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects.