picasso
Sparse Learning with Convex and Concave Penalties
Fast tools for fitting sparse generalized linear models with convex penalties (lasso) and concave penalties (smoothly clipped absolute deviation and minimax concave penalty). Computation uses multi-stage convex relaxation and pathwise coordinate optimization with warm starts, active-set updates, and screening rules. Core solvers are implemented in C++, and coefficient paths are stored as sparse matrices for memory efficiency.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.5 |
rolling linux/jammy R-4.5 | picasso_1.5.tar.gz |
5.7 MiB |
1.5 |
rolling linux/noble R-4.5 | picasso_1.5.tar.gz |
5.7 MiB |
1.5 |
rolling source/ R- | picasso_1.5.tar.gz |
9.1 MiB |
1.5 |
latest linux/jammy R-4.5 | picasso_1.5.tar.gz |
5.7 MiB |
1.5 |
latest linux/noble R-4.5 | picasso_1.5.tar.gz |
5.7 MiB |
1.5 |
latest source/ R- | picasso_1.5.tar.gz |
9.1 MiB |
1.5 |
2026-04-26 source/ R- | picasso_1.5.tar.gz |
9.1 MiB |
1.5 |
2026-04-23 source/ R- | picasso_1.5.tar.gz |
9.1 MiB |
1.5 |
2026-04-09 windows/windows R-4.5 | picasso_1.5.zip |
5.7 MiB |