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r / packages / r-glamlasso

Efficient design matrix free lasso penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The procedure is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the following models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. It is also possible to include a component in the model with non-tensor design e.g an intercept. Also provided are functions, glamlassoRR() and glamlassoS(), fitting special cases of GLAMs.

Type Size Name Uploaded Downloads Labels
conda 333.9 kB | linux-64/r-glamlasso-3.0.1-r43h884c59f_0.tar.bz2  1 year and 1 month ago 23 main
conda 335.6 kB | linux-64/r-glamlasso-3.0.1-r42h884c59f_0.tar.bz2  2 years and 7 months ago 50 main
conda 337.8 kB | win-64/r-glamlasso-3.0-r36h796a38f_0.tar.bz2  4 years and 11 months ago 78 main
conda 381.2 kB | osx-64/r-glamlasso-3.0-r36h466af19_0.tar.bz2  4 years and 11 months ago 14 main
conda 354.7 kB | linux-64/r-glamlasso-3.0-r36h29659fb_0.tar.bz2  4 years and 11 months ago 52 main

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