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' or a 'dsCMatrix' from R
package 'Matrix'.

person(given = "Florian", family = "Privé", role = c("aut", "cre"),
email = "florian.prive.21@gmail.com")
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' or a 'dsCMatrix' from R
package 'Matrix'. A new compact format is also now available.

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

Provide 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' or a 'dsCMatrix' from R
package 'Matrix'. A new compact format is also now available.
Rcpp, bigassertr, methods, Matrix, rmio (>= 0.4)

Big Splines (smoothing splines for large samples)
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.

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.