Highly Adaptive Lasso Conditional Density Estimation

Conditional density estimation is a longstanding and challenging problem in statistical theory, and numerous proposals exist for optimally estimating such complex functions. Algorithms for nonparametric estimation of conditional densities based on a pooled hazard regression formulation and semiparametric estimation via conditional hazards modeling are implemented based on the highly adaptive lasso, a nonparametric regression function for efficient estimation with fast convergence under mild assumptions. The pooled hazards formulation implemented was first described by Díaz and van der Laan (2011) .


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0.0.6 by Nima Hejazi, 4 months ago


Report a bug at https://github.com/nhejazi/haldensify/issues

Browse source code at https://github.com/cran/haldensify

Authors: Nima Hejazi [aut, cre, cph] , David Benkeser [aut] , Mark van der Laan [aut, ths]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports stats, ggplot2, data.table, matrixStats, future.apply, assertthat, hal9001, origami, Rdpack

Suggests testthat, knitr, rmarkdown, future, dplyr

Imported by txshift.

See at CRAN