FDboost - Boosting Functional Regression Models
Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.
Last updated 2 months ago
boostingboosting-algorithmsfunction-on-function-regressionfunction-on-scalar-regressionmachine-learningscalar-on-function-regressionvariable-selection
8.00 score 17 stars 98 scripts 877 downloadscAIC4 - Conditional Akaike Information Criterion for 'lme4' and 'nlme'
Provides functions for the estimation of the conditional Akaike information in generalized mixed-effect models fitted with (g)lmer() from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'. For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.
Last updated 3 years ago
3.20 score 2 stars 1 dependents 66 scripts 980 downloadsdeepregression - Fitting Deep Distributional Regression
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>. Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
Last updated 3 months ago
2.28 score 1 dependents 63 scripts 384 downloads