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picasso  

Sparse Learning with Convex and Concave Penalties
View on CRAN: Click here


Download and install picasso package within the R console
Install from CRAN:
install.packages("picasso")

Install from Github:
library("remotes")
install_github("cran/picasso")

Install by package version:
library("remotes")
install_version("picasso", "2.0.1")



Attach the package and use:
library("picasso")
Maintained by
Jason Ge
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-08-13
Latest Update:
Description:
Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output.
How to cite:
Jason Ge (2014). picasso: Sparse Learning with Convex and Concave Penalties. R package version 2.0.1, https://cran.r-project.org/web/packages/picasso. Accessed 26 Oct. 2026.
Previous versions and publish date:
(2026-07-25 23:00), 0.1.0 (2014-08-13 11:05), 0.2.0 (2014-10-06 08:14), 0.3.0 (2014-10-19 01:37), 0.4.7 (2015-09-15 16:22), 0.5.0 (2015-12-18 22:40), 0.5.4 (2016-10-06 20:14), 1.0.1 (2017-07-21 01:04), 1.2.0 (2017-12-20 18:45), 1.3.0 (2018-10-03 10:30), 1.3.1 (2019-02-21 22:20), 1.4.1 (2026-03-10 13:00), 1.5 (2026-03-12 15:00)
Other packages that cited picasso R package
View picasso citation profile
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Complete documentation for picasso
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