Imports: Matrix, stats, utils, methods, assertthat, origami (>= 1.0.3),

person("Rachael", "Phillips", email = "rachaelvphillips@berkeley.edu",
dplyr, tidyr, stringr, survival, SuperLearner

person("Lars", "van der Laan", email = "vanderlaanlars@yahoo.com",
given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>. This
implementation of the highly adaptive lasso algorithm was described by Hejazi,

Title: Highly Adaptive Lasso Conditional Density Estimation
Description: 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
Imports: stats, ggplot2, data.table, future.apply, assertthat, hal9001
Suggests: testthat, knitr, rmarkdown, future, dplyr
Author: Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>),
Mark van der Laan [aut, ths] (<https://orcid.org/0000-0003-1432-5511>)

Imports: stats, ggplot2, data.table, matrixStats, future.apply,
assertthat, hal9001 (>= 0.2.5), origami (>= 1.0.3), Rdpack

Description: An algorithm for flexible conditional density estimation based on
application of pooled hazard regression to an artificial repeated measures
dataset constructed by discretizing the support of the outcome variable. To
facilitate non/semi-parametric estimation of the conditional density, the
highly adaptive lasso, a nonparametric regression function shown to reliably
estimate a large class of functions at a fast convergence rate, is utilized.
The pooled hazards data augmentation formulation implemented was first
described by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. To
complement the conditional density estimation utilities, tools for efficient
nonparametric inverse probability weighted (IPW) estimation of the causal
effects of stochastic shift interventions (modified treatment policies),
directly utilizing the density estimation technique for construction of the
generalized propensity score, are provided. These IPW estimators utilize
undersmoothing (sieve estimation) of the conditional density estimators in
order to achieve the non/semi-parametric efficiency bound.
Imports: stats, utils, dplyr, tibble, ggplot2, data.table, matrixStats,
future.apply, assertthat, hal9001 (>= 0.4.1), origami (>=
1.0.3), rsample, rlang, latex2exp, scales, Rdpack
Suggests: testthat, knitr, rmarkdown, stringr, covr, future
Mark van der Laan [aut, ths] (<https://orcid.org/0000-0003-1432-5511>),