r-pls 2.8-0 Partial Least Squares and Principal Component Regression

The pls package implements multivariate regression methods: Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Canonical Powered Partial Least Squares (CPPLS). It supports:

  • several algorithms: the traditional orthogonal scores (NIPALS) PLS algorithm, kernel PLS, wide kernel PLS, Simpls, and PCR through svd

  • multi-response models (or PLS2)

  • flexible cross-validation

  • Jackknife variance estimates of regression coefficients

  • extensive and flexible plots: scores, loadings, predictions, coefficients, (R)MSEP, R², and correlation loadings

  • formula interface, modelled after lm(), with methods for predict, print, summary, plot, update, etc.

  • extraction functions for coefficients, scores, and loadings

  • MSEP, RMSEP, and R² estimates

  • multiplicative scatter correction (MSC)