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

bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.2.3),
bigreadr, bigutilsr (>= 0.3), data.table, foreach, ggplot2,
magrittr, Matrix, methods, Rcpp, stats
bindata, covr, dbplyr (>= 1.4), dplyr, gaston, glue, Hmisc,
pcadapt (>= 4.1), rmutil, RSpectra, RSQLite, R.utils, spelling,
testthat, xgboost

bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.4),
bigreadr, bigutilsr (>= 0.3), data.table, foreach, ggplot2,
magrittr, Matrix, methods, Rcpp, stats, vctrs

bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.4.1),
bigreadr, bigutilsr (>= 0.3), data.table, foreach, ggplot2,
magrittr, Matrix, methods, Rcpp, stats, vctrs

c(
person("Florian", "Privé", email = "florian.prive.21@gmail.com",
role = c("aut", "cre")),
person("Michael", "Blum", email = "michael.blum@univ-grenoble-alpes.fr",
role = "ths"),
person("Hugues", "Aschard", email = "hugues.aschard@pasteur.fr",
role = "ths"))
bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.4.3),
bigreadr, bigutilsr (>= 0.3.3), data.table, foreach, ggplot2,
magrittr, Matrix, methods, Rcpp, stats, vctrs
bindata, covr, dbplyr (>= 1.4), dplyr, gaston, glue, Hmisc,
pcadapt (>= 4.1), rmutil, RSpectra, RSQLite, R.utils, spelling,
testthat, tibble, xgboost

c(
person("Florian", "Privé", email = "florian.prive.21@gmail.com", role = c("aut", "cre")),
person("Michael", "Blum", email = "michael.blum@univ-grenoble-alpes.fr", role = "ths"),
person("Hugues", "Aschard", email = "hugues.aschard@pasteur.fr", role = "ths"),
person("Bjarni Jóhann", "Vilhjálmsson", email = "bjv@econ.au.dk", role = "ths"))
bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.5),
bigreadr, bigutilsr (>= 0.3.3), data.table, foreach, ggplot2,
magrittr, Matrix, methods, Rcpp, stats, vctrs

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'.