c(
person("Greg Freedman Ellis", role = "aut"),
person("Derek Burk", email = "ipums+cran@umn.edu", role = c("aut", "cre")),
person("Joe Grover", role = "ctb"),
person("Finn Roberts", role = "ctb"),
person("Dan Ehrlich", role = "ctb"),
person("Renae Rodgers", role = "ctb"),
person("Minnesota Population Center", email = "ipums@umn.edu", role = "cph")
)
dplyr (>= 0.7.0), haven (>= 2.2.0), hipread (>= 0.2.0), purrr,
R6, readr, rlang, tibble, tidyselect, xml2, zeallot, jsonlite,
httr
DT, ggplot2, htmltools, knitr, rgdal, rmarkdown, rstudioapi,
scales, sf, sp, shiny, testthat, covr, biglm, DBI, RSQLite,
dbplyr, vcr (>= 0.6.0), withr, raster
Greg Freedman Ellis [aut],
Derek Burk [aut, cre],
Joe Grover [ctb],
Finn Roberts [ctb],
Dan Ehrlich [ctb],
Renae Rodgers [ctb],
Minnesota Population Center [cph]

Willem M. van der Wal <w.m.vanderwal@amc.uva.nl>
R (>= 2.0.1), boot, geepack, MASS, nlme, nnet, survival
Estimate inverse probability weights. These are typically
used to perform inverse probability weighting (IPW) to fit a
marginal structural model (MSM), to estimate causal effects
from observational data. Both data from point treatment
situations and longitudinal studies can be used.

Willem M. van der Wal <w.m.vd.wal@umcutrecht.nl>
R (>= 2.0.0), boot, geepack, MASS, nlme, nnet, survey,
survival
Willem M. van der Wal
<w.m.vd.wal@umcutrecht.nl>
Orphaned on 2013-12-21 as maintainer address
<w.m.vd.wal@umcutrecht.nl> bounced.

c(person(c("Ronald", "B."), "Geskus", role=c("aut", "cre"),
, email="r.b.geskus@amc.uva.nl"),
person(c("Willem","M."), "van der Wal", role=c("aut"),
comment="maintainer until 2010"))
Ronald B. Geskus [aut, cre],
Willem M. van der Wal [aut] (maintainer until 2010)
MASS, nnet, survival, geepack, graphics, methods, stats
Functions to estimate the probability to receive the observed treatment, based on
individual characteristics. The inverse of these probabilities can be used as weights when
estimating causal effects from observational data via marginal structural models. Both point
treatment situations and longitudinal studies can be analysed. The same functions can be used to
correct for informative censoring.