Packaged: Tue Nov  2 15:00:07 2004; zeileis

Packaged: Mon Nov 29 12:23:21 2004; zeileis

Title: Non-Parametric Causal Effects of Feasible Interventions Based on
Description: Non-parametric estimators for casual effects based on longitudinal modified treatment
policies as described in Diaz, Williams, and Hoffman (<arXiv:2006.01366>), traditional point treatment,
and traditional longitudinal effects. Continuous, binary, and categorical treatments are allowed as well are
censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure
irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects
can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Estimation is enhanced
using the Super Learner from 'sl3' available for download from GitHub using 'remotes::install_github("tlverse/sl3@devel")'.
Imports: slider, stats, nnls, cli, utils, R6, generics, origami, future
Suggests: testthat (>= 2.1.0), covr, rmarkdown, knitr, ranger, twang
Author: Nicholas Williams [aut, cre, cph]

can be calculated and relative risks and odds ratios may be calculated for binary outcomes.
Imports: stats, nnls, cli, utils, R6, generics, origami, future (>=

Suggests: testthat (>= 2.1.0), covr, rmarkdown, knitr, ranger, twang,
Packaged: 2021-08-16 19:27:29 UTC; nickwilliams

policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment,
Packaged: 2021-09-28 17:16:01 UTC; nickwilliams

Imports: stats, nnls, cli, R6, generics, origami, future (>= 1.17.0),
progressr, data.table, checkmate (>= 2.1.0), SuperLearner
Packaged: 2022-05-21 00:32:02 UTC; nickwilliams

progressr, data.table (>= 1.13.0), checkmate (>= 2.1.0),

Title: Linear Regression with Non-Constant Variances
Description: Runs a linear regression in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.