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Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2019) <arXiv:1909.11784>.

copied from cf-staging / r-bamlss
Type Size Name Uploaded Downloads Labels
conda 4.6 MB | win-64/r-bamlss-1.2_2-r41hb9981e2_0.conda  1 year and 4 months ago 492 main
conda 4.6 MB | osx-64/r-bamlss-1.2_2-r42h39411cf_0.conda  1 year and 4 months ago 314 main
conda 4.6 MB | osx-64/r-bamlss-1.2_2-r43h39411cf_0.conda  1 year and 4 months ago 319 main
conda 4.6 MB | linux-64/r-bamlss-1.2_2-r42h316c678_0.conda  1 year and 4 months ago 1325 main
conda 4.6 MB | linux-64/r-bamlss-1.2_2-r43h316c678_0.conda  1 year and 4 months ago 1209 main

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