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Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks.
| Version: | 1.1.3 |
| Depends: | R (≥ 2.14), splines |
| Imports: | Rcpp |
| LinkingTo: | Rcpp, RcppEigen |
| Published: | 2021-07-01 |
| DOI: | 10.32614/CRAN.package.SAM |
| Author: | Haoming Jiang, Yukun Ma, Han Liu, Kathryn Roeder, Xingguo Li, and Tuo Zhao |
| Maintainer: | Haoming Jiang <jianghm.ustc at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | SAM results |
| Reference manual: | SAM.pdf |
| Package source: | SAM_1.1.3.tar.gz |
| Windows binaries: | r-devel: SAM_1.1.3.zip, r-release: SAM_1.1.3.zip, r-oldrel: SAM_1.1.3.zip |
| macOS binaries: | r-release (arm64): SAM_1.1.3.tgz, r-oldrel (arm64): SAM_1.1.3.tgz, r-release (x86_64): SAM_1.1.3.tgz, r-oldrel (x86_64): SAM_1.1.3.tgz |
| Old sources: | SAM archive |
| Reverse imports: | DLL, GSelection, pgraph, varEst |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.