Fast Algorithms for Best Subset Selection
Highly optimized toolkit for (approximately) solving L0-regularized learning problems.
The algorithms are based on coordinate descent and local combinatorial search.
For more details, check the paper by Hazimeh and Mazumder (2018) <arXiv:1803.01454>;
the link is provided in the URL field below.
Rcpp (>= 0.12.13), Matrix, methods, ggplot2, reshape2

Highly optimized toolkit for approximately solving L0-regularized learning problems (aka best subset selection).
The algorithms are based on coordinate descent and local combinatorial search.
For more details, check the paper by Hazimeh and Mazumder (2018) <arXiv:1803.01454>;
the link is provided in the URL field below.

c(
person("Hussein", "Hazimeh", email = "hazimeh@mit.edu", role = c("aut", "cre")),
person("Rahul", "Mazumder", email = "rahulmaz@mit.edu", role = "aut"),
person("Tim", "Nonet", email = "tim.nonet@gmail.com", role = "aut"))
Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection).
The algorithms are based on coordinate descent and local combinatorial search.
For more details, check the paper by Hazimeh and Mazumder (2020);
the link is provided in the URL field below.
Rcpp (>= 0.12.13), Matrix, methods, ggplot2, reshape2, MASS
knitr, rmarkdown, testthat, pracma, raster, tilting
Hussein Hazimeh [aut, cre],
Rahul Mazumder [aut],
Tim Nonet [aut]

knitr, rmarkdown, testthat, pracma, raster

Fitting the regularization path of the L1-regularized multi-state models since they can exploit sparsity structure of input. Different tuning regularization parameter methods are provided. The cumulative hazard rate estimation and the transition probability predictions can be made from the fitted models.
Matrix (>= 1.2-10), MASS, mstate, colorspace

L1 estimation for linear regression and random number generation for
the multivariate Laplace distribution.

c(person("Felipe", "Osorio", role = c("aut", "cre"), email = "felipe.osorios@usm.cl"),
person("Tymoteusz", "Wolodzko", role = "aut"))
L1 estimation for linear regression, density, distribution function,
quantile function and random number generation for univariate and multivariate
Laplace distribution.
Felipe Osorio [aut, cre], Tymoteusz Wolodzko [aut]