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hofnerb
stabs:Stability Selection with Error Control
Resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>) and complementary pairs stability selection with improved error bounds (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are implemented. The package can be combined with arbitrary user specified variable selection approaches.
Maintained by Benjamin Hofner. Last updated 4 years ago.
machine-learningr-languageresamplingstability-selectionvariable-importancevariable-selection
26 stars 9.59 score 53 scripts 31 dependents
rjacobucci
regsem:Regularized Structural Equation Modeling
Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed).
Maintained by Ross Jacobucci. Last updated 2 years ago.
14 stars 6.63 score 77 scripts
freezenik
bamlss:Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
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 (2021) <doi:10.18637/jss.v100.i04>.
Maintained by Nikolaus Umlauf. Last updated 6 months ago.
1 stars 5.76 score 239 scripts 5 dependents
fbertran
c060:Extended Inference for Lasso and Elastic-Net Regularized Cox and Generalized Linear Models
The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.
Maintained by Frederic Bertrand. Last updated 2 years ago.
3 stars 4.35 score 37 scripts