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cvLM

Cross-Validation for Linear and Ridge Regression Models

Implements cross-validation methods for linear and ridge regression models. The package provides grid-based selection of the ridge penalty parameter using Singular Value Decomposition (SVD) and supports K-fold cross-validation, Leave-One-Out Cross-Validation (LOOCV), and Generalized Cross-Validation (GCV). Computations are implemented in C++ via 'RcppArmadillo' with optional parallelization using 'RcppParallel'. The methods are suitable for high-dimensional settings where the number of predictors exceeds the number of observations.

Versions across snapshots

VersionRepositoryFileSize
2.0.0 rolling linux/jammy R-4.5 cvLM_2.0.0.tar.gz 185.3 KiB
2.0.0 rolling linux/noble R-4.5 cvLM_2.0.0.tar.gz 192.1 KiB
2.0.0 rolling source/ R- cvLM_2.0.0.tar.gz 37.7 KiB
2.0.0 latest linux/jammy R-4.5 cvLM_2.0.0.tar.gz 185.3 KiB
2.0.0 latest linux/noble R-4.5 cvLM_2.0.0.tar.gz 192.1 KiB
2.0.0 latest source/ R- cvLM_2.0.0.tar.gz 37.7 KiB
2.0.0 2026-04-26 source/ R- cvLM_2.0.0.tar.gz 37.7 KiB
2.0.0 2026-04-23 source/ R- cvLM_2.0.0.tar.gz 37.7 KiB
2.0.0 2026-04-09 windows/windows R-4.5 cvLM_2.0.0.zip 497.6 KiB
1.0.4 2025-04-20 source/ R- cvLM_1.0.4.tar.gz 14.0 KiB

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