Package: gfoRmula
Title: Parametric G-Formula
Version: 1.0.0
Authors@R: c(person("Victoria", "Lin", role = c("aut"), 
                     email = "vlin@alumni.harvard.edu", 
                     comment = "V. Lin and S. McGrath made equal 
                     contributions"),
              person("Sean", "McGrath", role = c("aut", "cre"),
                      email = "sean_mcgrath@g.harvard.edu", 
                      comment = c(ORCID = "0000-0002-7281-3516", 
                      "V. Lin and S. McGrath made equal contributions")),
              person("Zilu", "Zhang", role = c("aut"),
                      email = "zilu_zhang@dfci.harvard.edu"),
              person("Roger W.", "Logan", role = c("aut"),
                      email = "rwlogan@hsph.harvard.edu"),
              person("Lucia C.", "Petito", role = c("aut"),
                      email = "petito@hsph.harvard.edu"),
              person("Jessica G.", "Young", role = c("aut"),
                      email = "jyoung@hsph.harvard.edu",
                      comment = c(ORCID = "0000-0002-2758-6932", 
                      "M.A. Hernán and J.G. Young made equal contributions")),
              person("Miguel A.", "Hernán", role = c("aut"),
                      email = "mhernan@hsph.harvard.edu",
                      comment = "M.A. Hernán and J.G. Young made equal  
                      contributions"),
              person("2019 The President and Fellows of Harvard College", 
                      role = c("cph")))
Description: Implements the parametric g-formula algorithm of Robins (1986) 
    <doi:10.1016/0270-0255(86)90088-6>. The g-formula can be used to estimate 
    the causal effects of hypothetical time-varying treatment interventions on 
    the mean or risk of an outcome from longitudinal data with time-varying 
    confounding. This package allows: 1) binary or continuous/multi-level 
    time-varying treatments; 2) different types of outcomes (survival or 
    continuous/binary end of follow-up); 3) data with competing events or 
    truncation by death and loss to follow-up and other types of censoring 
    events; 4) different options for handling competing events in the case of 
    survival outcomes; 5) a random measurement/visit process; 6) joint 
    interventions on multiple treatments; and 7) general incorporation of a 
    priori knowledge of the data structure.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
Imports: data.table, ggplot2, ggpubr, grDevices, nnet, parallel,
        progress, stats, stringr, survival, truncnorm, truncreg, utils
Suggests: Hmisc
URL: https://github.com/CausalInference/gfoRmula,
        https://www.cell.com/patterns/fulltext/S2666-3899(20)30008-8
BugReports: https://github.com/CausalInference/gfoRmula/issues
NeedsCompilation: no
Packaged: 2022-04-09 19:41:41 UTC; Sean
Author: Victoria Lin [aut] (V. Lin and S. McGrath made equal contributions),
  Sean McGrath [aut, cre] (<https://orcid.org/0000-0002-7281-3516>, V.
    Lin and S. McGrath made equal contributions),
  Zilu Zhang [aut],
  Roger W. Logan [aut],
  Lucia C. Petito [aut],
  Jessica G. Young [aut] (<https://orcid.org/0000-0002-2758-6932>, M.A.
    Hernán and J.G. Young made equal contributions),
  Miguel A. Hernán [aut] (M.A. Hernán and J.G. Young made equal
    contributions),
  2019 The President and Fellows of Harvard College [cph]
Maintainer: Sean McGrath <sean_mcgrath@g.harvard.edu>
Repository: CRAN
Date/Publication: 2022-04-09 20:02:28 UTC
Built: R 4.0.5; ; 2022-04-21 16:07:05 UTC; windows
