R: Partial Least Squares Regression Models with Big Matrices

# Partial Least Squares Regression Models with Big Matrices

## Documentation for package ‘bigPLSR’ version 0.7.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

bigPLSR-package | bigPLSR-package
.finalize_pls_fit | Finalize pls objects
bigPLSR | bigPLSR-package
bigPLSR_stream_kstats | Streamed centering statistics for RKHS kernels
cpp_irls_binomial | Fast IRLS for binomial logit with class weights
cpp_kernel_pls | Internal kernel and wide-kernel PLS solver
external_pls_benchmarks | Benchmark results against external PLS implementations
kf_pls_state_fit | Finalize a KF-PLS state into a fitted model
kf_pls_state_new | KF-PLS streaming state (constructor)
kf_pls_state_update | Update a KF-PLS streaming state with a mini-batch
plot_pls_biplot | PLS biplot
plot_pls_bootstrap_coefficients | Boxplots of bootstrap coefficient distributions
plot_pls_bootstrap_scores | Boxplots of bootstrap score distributions
plot_pls_individuals | Plot individual scores
plot_pls_variables | Plot variable loadings
plot_pls_vip | Plot Variable Importance in Projection (VIP)
pls_bootstrap | Bootstrap a PLS model
pls_cross_validate | Cross-validate PLS models
pls_cv_select | Select components from cross-validation results
pls_fit | Unified PLS fit with auto backend and selectable algorithm
pls_information_criteria | Compute information criteria for component selection
pls_predict_response | Predict responses from a PLS fit
pls_predict_scores | Predict latent scores from a PLS fit
pls_select_components | Component selection via information criteria
pls_threshold | Naive sparsity control by coefficient thresholding
pls_vip | Variable importance in projection (VIP) scores
predict.big_plsr | Predict method for big_plsr objects
print.summary.big_plsr | Print a 'summary.big_plsr' object
summarise_pls_bootstrap | Summarise bootstrap estimates
summary.big_plsr | Summarize a 'big_plsr' model