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Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>. Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of gam() in package 'mgcv' are used for setting up the model matrix, printing and plotting the results. Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with multiple smoothing parameter selection by GCV or UBRE/AIC.

copied from cf-staging / r-scam
Type Size Name Uploaded Downloads Labels
conda 977.0 kB | win-64/r-scam-1.2_15-r41h6d2157b_0.conda  10 months and 17 days ago 143 main
conda 971.5 kB | osx-64/r-scam-1.2_15-r43hb2c329c_0.conda  10 months and 17 days ago 134 main
conda 974.5 kB | osx-64/r-scam-1.2_15-r42hb2c329c_0.conda  10 months and 17 days ago 153 main
conda 973.5 kB | linux-64/r-scam-1.2_15-r43h57805ef_0.conda  10 months and 17 days ago 905 main
conda 976.1 kB | linux-64/r-scam-1.2_15-r42h57805ef_0.conda  10 months and 17 days ago 891 main

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