Package: sMTL 0.1.0

sMTL: Sparse Multi-Task Learning

Implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <arxiv:2212.08697>.

Authors:Gabriel Loewinger [aut, cre], Kayhan Behdin [aut], Giovanni Parmigiani [aut], Rahul Mazumder [aut], National Science Foundation Grant DMS1810829 [fnd], National Science Foundation Grant DMS2113707 [fnd], National Science Foundation Grant NSF-IIS1718258, [fnd], Office of Naval Research Grant ONR N000142112841 [fnd], National Institute on Drug Abuse Grant F31DA052153 [fnd]

sMTL_0.1.0.tar.gz
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sMTL_0.1.0.tgz(r-4.4-any)sMTL_0.1.0.tgz(r-4.3-any)
sMTL_0.1.0.tar.gz(r-4.5-noble)sMTL_0.1.0.tar.gz(r-4.4-noble)
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sMTL.pdf |sMTL.html✨
sMTL/json (API)

Peer review:

Bug tracker:https://github.com/gloewing/smtl/issues

On CRAN:

16 exports 0.00 score 85 dependencies 8 scripts 141 downloads

Last updated 2 years agofrom:2aa7aea46d. Checks:OK: 7. Indexed: yes.

Exports:cv.smtlgrid.genmaxEigenmethod_nmmultiTaskRmsemultiTaskRmse_MTpredictreName_cvrhoScaleseReturnsmtlsmtl_setupsparseCVsparseCV_MTsparseL0Tn_ihttuneZscale

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071evaluatefansifarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhathighripredisobanditeratorsJuliaCallJuliaConnectoRKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rjsonrlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxfunyaml