| dual.spls-package | dual.spls package |
| d.spls.calval | Splits data into calibration and validation sets using the splitting method CalValXy that takes into account X and y |
| d.spls.cv | Determination of the number of latent components to be used in a Dual-SPLS regression |
| d.spls.GL | Dual Sparse Partial Least Squares (Dual-SPLS) regression for the group lasso norms |
| d.spls.lasso | Dual Sparse Partial Least Squares (Dual-SPLS) regression for the lasso norm |
| d.spls.LS | Dual Sparse Partial Least Squares (Dual-SPLS) regression for the least squares norm |
| d.spls.metric | Computes predictions criterias |
| d.spls.NIR | Dual Sparse Partial Least Squares (Dual-SPLS) Near Infrared data |
| d.spls.plot | Plots the coefficient curve of a Dual-SPLS regression |
| d.spls.pls | Univariate Partial Least Squares regression |
| d.spls.predict | Makes predictions from a fitted Dual-SPLS model |
| d.spls.print | Print function for Dual-SPLS models |
| d.spls.ridge | Dual Sparse Partial Least Squares (Dual-SPLS) regression for the ridge norm |
| d.spls.simulate | Simulation of a data |
| dual.spls | dual.spls package |