Package: stdReg2
Type: Package
Title: Regression Standardization for Causal Inference
Version: 1.0.8
Authors@R: 
    c(person(given = "Michael C",
             family = "Sachs",
             role = c("aut", "cre"),
             email = "sachsmc@gmail.com"),
      person(given = "Arvid", 
             family = "Sjölander", role = c("aut")), 
      person(given = "Erin E", 
      family = "Gabriel", 
      role = "aut"),
      person(given = "Johan Sebastian", 
            family = "Ohlendorff", role = "aut"), 
      person(given = "Adam", 
            family = "Brand", role = "aut"))
URL: https://sachsmc.github.io/stdReg2/,
        https://github.com/sachsmc/stdReg2
BugReports: https://github.com/sachsmc/stdReg2/issues
Date: 2026-05-28
Description: Contains more modern tools for causal inference using
        regression standardization. Four general classes of models are
        implemented; generalized linear models, conditional generalized
        estimating equation models, Cox proportional hazards models,
        and shared frailty gamma-Weibull models. Methodological details
        are described in Sjölander, A. (2016)
        <doi:10.1007/s10654-016-0157-3>. Also includes functionality
        for doubly robust estimation for generalized linear models in
        some special cases, and the ability to implement custom models.
License: AGPL (>= 3)
Encoding: UTF-8
Imports: data.table, drgee, generics, survival, sandwich
Suggests: causaldata, knitr, nnet, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
RoxygenNote: 7.3.3
Depends: R (>= 4.1.0)
LazyData: true
Repository: https://sachsmc.r-universe.dev
Date/Publication: 2026-05-28 13:24:12 UTC
RemoteUrl: https://github.com/sachsmc/stdReg2
RemoteRef: HEAD
RemoteSha: 45899e4963e090882d7a55d9a4f74e00a3085618
NeedsCompilation: no
Packaged: 2026-07-03 13:53:01 UTC; root
Author: Michael C Sachs [aut, cre],
  Arvid Sjölander [aut],
  Erin E Gabriel [aut],
  Johan Sebastian Ohlendorff [aut],
  Adam Brand [aut]
Maintainer: Michael C Sachs <sachsmc@gmail.com>