R6, readr, rlang, tibble, tidyselect, xml2, zeallot, jsonlite,

Package: ipw
Version: 1.0-1
Date: 2010-02-08
Title: Estimate inverse probability weights.
Author: Willem M. van der Wal <w.m.vanderwal@amc.uva.nl>
Maintainer: Willem M. van der Wal <w.m.vanderwal@amc.uva.nl>
Depends: R (>= 2.0.1), boot, geepack, MASS, nlme, nnet, survival
Description: 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.
Encoding: latin1
License: GPL (>= 2)
Packaged: 2010-02-08 09:48:29 UTC; wwal
Repository: CRAN
Date/Publication: 2010-02-08 10:18:53

Author: Willem M. van der Wal <w.m.vd.wal@umcutrecht.nl>
Depends: R (>= 2.0.0), boot, geepack, MASS, nlme, nnet, survey,
X-CRAN-Original-Maintainer: Willem M. van der Wal

Title: Estimate Inverse Probability Weights
Authors@R: c(person(c("Ronald", "B."), "Geskus", role=c("aut", "cre"),
person(c("Willem","M."), "van der Wal", role=c("aut"),
Willem M. van der Wal [aut] (maintainer until 2010)
Maintainer: Ronald B. Geskus <r.b.geskus@amc.uva.nl>
Imports: MASS, nnet, survival, geepack, graphics, methods, stats
Description: 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

Package: ipw
Version: 1.0-2
Date: 2010-02-08
Title: Estimate inverse probability weights.
Author: Willem M. van der Wal <w.m.vanderwal@amc.uva.nl>
Maintainer: Willem M. van der Wal <w.m.vanderwal@amc.uva.nl>
Depends: R (>= 2.0.0), boot, geepack, MASS, nlme, nnet, survival
Description: 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.
Encoding: latin1
License: GPL (>= 2)
Packaged: 2010-02-13 17:24:17 UTC; wwal
Repository: CRAN
Date/Publication: 2010-02-14 14:44:23