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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 3.9 MB | win-64/r-bamlss-1.2_5-r44he7dbcbb_0.conda  4 months and 21 days ago 263 main
conda 3.9 MB | win-64/r-bamlss-1.2_5-r43he7dbcbb_0.conda  4 months and 21 days ago 264 main
conda 3.9 MB | osx-64/r-bamlss-1.2_5-r44hfa521a7_0.conda  4 months and 21 days ago 123 main
conda 3.9 MB | linux-64/r-bamlss-1.2_5-r44h02ebe12_0.conda  4 months and 21 days ago 554 main
conda 3.9 MB | osx-64/r-bamlss-1.2_5-r43hfa521a7_0.conda  4 months and 21 days ago 133 main
conda 3.9 MB | linux-64/r-bamlss-1.2_5-r43h02ebe12_0.conda  4 months and 21 days ago 534 main

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