Package index • OHPL

Fit OHPL models

Fit OHPL models.

OHPL(): Ordered Homogeneity Pursuit Lasso

FOP(): Fisher optimal partition

proto(): Extract the prototype from each variable group

dlc(): Compute D, L, and C in the Fisher optimal partitions algorithm

OHPL-package: OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Evaluate OHPL models

Cross-validation, prediction, performance evaluation, and generation of simulated data.

cv.OHPL(): Cross-validation for Ordered Homogeneity Pursuit Lasso

predict(<OHPL>): Make predictions based on the fitted OHPL model

OHPL.RMSEP(): Compute RMSEP, MAE, and Q2 for a test set

OHPL.sim(): Generate simulation data for benchmarking sparse regressions (Gaussian response)

Datasets

Real-world spectroscopic datasets used in the paper.

beer: The beer dataset

wheat: The wheat dataset

soil: The soil dataset