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| author | Naga Malleswari <nagamalli@riseup.net> | 2020-04-24 01:37:05 +0530 |
|---|---|---|
| committer | Ricardo Wurmus <rekado@elephly.net> | 2020-04-24 15:51:39 +0200 |
| commit | aa3fdca85c6a28e8a945ac0041b921465fa0fb66 (patch) | |
| tree | 44c5268f7f1c298c0283883d5e00a47b61c093e9 /gnu/packages/cran.scm | |
| parent | ea43d299fa2071467cb1aec8cf3dc8f0d95b15f7 (diff) | |
| download | patches-aa3fdca85c6a28e8a945ac0041b921465fa0fb66.tar patches-aa3fdca85c6a28e8a945ac0041b921465fa0fb66.tar.gz | |
gnu: Add r-sgloptim.
* gnu/packages/cran.scm (r-sgloptim): New variable.
Signed-off-by: Ricardo Wurmus <rekado@elephly.net>
Diffstat (limited to 'gnu/packages/cran.scm')
| -rw-r--r-- | gnu/packages/cran.scm | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index f0796891c8..d023c47705 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -21242,3 +21242,39 @@ Propagation-Separation approach to adaptive smoothing, the @dfn{Intersecting Confidence Intervals} (ICI), variational approaches, and a non-local means filter.") (license license:gpl2+))) + +(define-public r-sgloptim + (package + (name "r-sgloptim") + (version "1.3.8") + (source + (origin + (method url-fetch) + (uri (cran-uri "sglOptim" version)) + (sha256 + (base32 + "15bkkvgp9v9vsp65wps48g3c2fa0fj1025hbrziywq14j7wayyjr")))) + (properties + `((upstream-name . "sglOptim"))) + (build-system r-build-system) + (propagated-inputs + `(("r-bh" ,r-bh) + ("r-doparallel" ,r-doparallel) + ("r-foreach" ,r-foreach) + ("r-matrix" ,r-matrix) + ("r-rcpp" ,r-rcpp) + ("r-rcpparmadillo" ,r-rcpparmadillo) + ("r-rcppprogress" ,r-rcppprogress))) + (native-inputs + `(("r-knitr" ,r-knitr))) + (home-page "https://github.com/nielsrhansen/sglOptim") + (synopsis "Generic sparse group Lasso solver") + (description + "This package provides a fast generic solver for sparse group lasso +optimization problems. The loss (objective) function must be defined in a C++ +module. The optimization problem is solved using a coordinate gradient +descent algorithm. Convergence of the algorithm is established and the +algorithm is applicable to a broad class of loss functions. Use of parallel +computing for cross validation and subsampling is supported through the +@code{foreach} and @code{doParallel} packages.") + (license license:gpl2+))) |