LinkingTo: bigstatsr, Rcpp, RcppArmadillo (>= 0.9.600), rmio

Imports: bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.2.3),
bigreadr, bigutilsr (>= 0.3), data.table, foreach, ggplot2,
pcadapt (>= 4.1), rmutil, RSpectra, RSQLite, R.utils, spelling,

Imports: bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.4),
magrittr, Matrix, methods, Rcpp, stats, vctrs

Imports: bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.4.1),

person("Florian", "Privé", email = "florian.prive.21@gmail.com",
person("Michael", "Blum", email = "michael.blum@univ-grenoble-alpes.fr",
person("Hugues", "Aschard", email = "hugues.aschard@pasteur.fr",
Imports: bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.4.3),
bigreadr, bigutilsr (>= 0.3.3), data.table, foreach, ggplot2,

person("Michael", "Blum", email = "michael.blum@univ-grenoble-alpes.fr", role = "ths"),
Imports: bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.5),

Title: Sparse Matrix Format with Data on Disk
Description: Provides a sparse matrix format with data stored on disk, to be
used in both R and C++. This is intended for more efficient use of sparse
data in C++ and also when parallelizing, since data on disk does not need
copying. Only a limited number of features will be implemented. For now,
conversion can be performed from a 'dgCMatrix' of R package 'Matrix'.

LinkingTo: Rcpp, RcppEigen, rmio (>= 0.2)

conversion can be performed from a 'dgCMatrix' or a 'dsCMatrix' from R

person(given = "Florian", family = "Privé", role = c("aut", "cre"),
package 'Matrix'. A new compact format is also now available.
LinkingTo: Rcpp, RcppEigen, rmio (>= 0.3)

Imports: Rcpp, bigassertr, methods, rmio (>= 0.4)

Description: Provide a sparse matrix format with data stored on disk, to be
Imports: Rcpp, bigassertr, methods, Matrix, rmio (>= 0.4)

Title: Big Splines (smoothing splines for large samples)
Author: Nathaniel E. Helwig <nhelwig2@illinois.edu>
Maintainer: Nathaniel E. Helwig <nhelwig2@illinois.edu>
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.