Title: Smoothing splines for large samples

Description: Fits smoothing spline regression models using scalable algorithms designed for large samples. Five marginal spline types are supported: cubic, different cubic, cubic periodic, cubic thin-plate, and nominal. Parametric predictors are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Title: Smoothing Splines for Large Samples

Description: Fits smoothing spline regression models using scalable algorithms designed for large samples. Five marginal spline types are supported: cubic, different cubic, cubic periodic, cubic thin-plate, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Description: Fits smoothing spline regression models using scalable algorithms designed for large samples. Six marginal spline types are supported: cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Description: Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Title: Statistical Tools for Filebacked Big Matrices
Description: Easy-to-use, efficient, flexible and scalable statistical tools.
Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.
It provides for instance matrix operations, Principal Component Analysis,
sparse linear supervised models, utility functions and more.
A scientific paper associated with this package is in preparation.
Imports: cowplot, doParallel, foreach, ggplot2, glue, graphics,
magrittr, Matrix, methods, parallel, Rcpp, RSpectra, stats
Suggests: biglasso, bigmemory, covr, glmnet, grid, LiblineaR,
Collate: 'AUC.R' 'CMSA.R' 'FBM-attach.R' 'crochet.R' 'FBM.R'
'counts.R' 'crossprodSelf.R' 'plot.R' 'predict.R' 'randomSVD.R'