Showing 5 of total 5 results (show query)
gavinsimpson
gratia:Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Maintained by Gavin L. Simpson. Last updated 12 hours ago.
distributional-regressiongamgammgeneralized-additive-mixed-modelsgeneralized-additive-modelsggplot2glmlmmgcvpenalized-splinerandom-effectssmoothingsplines
216 stars 12.95 score 1.6k scripts 2 dependents
boost-r
mboost:Model-Based Boosting
Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.
Maintained by Torsten Hothorn. Last updated 5 months ago.
boosting-algorithmsgamglmmachine-learningmboostmodellingr-languagetutorialsvariable-selectionopenblas
72 stars 12.70 score 540 scripts 27 dependents
hsbadr
additive:Bindings for Additive TidyModels
Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels' via 'additive' <doi:10.5281/zenodo.4784245>. 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>). The technical details of 'mgcv' are described in Wood (2017) <doi:10.1201/9781315370279>.
Maintained by Hamada S. Badr. Last updated 2 months ago.
additivebamgamgeneralized-additive-modelsmgcvtidymodels
7 stars 5.99 score 14 scripts
inesortega
neuralGAM:Interpretable Neural Network Based on Generalized Additive Models
Neural network framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.
Maintained by Ines Ortega-Fernandez. Last updated 7 months ago.
deep-neural-networksexplainable-aigamganngeneralized-additive-modelsgeneralized-additive-neural-networkself-explanatory-mlxai
2 stars 5.44 score 40 scripts
pedersen-fisheries-lab
sspm:Spatial Surplus Production Model Framework for Northern Shrimp Populations
Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).
Maintained by Valentin Lucet. Last updated 2 months ago.
3 stars 5.28 score 21 scripts