DESCRIPTION

Package: causalglm
Type: Package
Title: Interpretable and robust causal inference for heterogeneous treatment effects using generalized linear models with targeted machine-learning. 
Version: 0.1.0
Author: Lars van der Laan
Maintainer: Lars van der Laan <vanderlaanlars@yahoo.com>
Description: Utilizing the framework of Targeted Maximum-Likelihood estimation (TMLE) and machine-learning,
  robust and efficient estimates and inference can be obtained for user-specified semiparametric and nonparametric generalized linear models including:
  Conditional odds ratios between a binary outcome and binary treatment variables (causal semiparametric logisic regression)
  Conditional additive treatment effects for a continuous outcome (causal semiparametric linear regression with general link functions)
  Conditional relative risk/treatment-effects for a nonnegative outcome (e.g. binary or count) (causal semiparametric relative risk regression with general link functions)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: 
  sl3,
  hal9001,
  stats,
  data.table,
  R6,
  doMC,
  tmle3
Depends:
  sl3,
  hal9001,
  stats,
  data.table,
  R6,
  tmle3
RoxygenNote: 7.1.1
Remotes: github::tlverse/hal9001@master, github::tlverse/sl3@Larsvanderlaan-formula_fix, github::tlverse/tmle3@categoricalNP
Suggests: 
    testthat (>= 3.0.0)
Config/testthat/edition: 3