Package: ipw
Version: 1.0-9
Date: 2011-04-02
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, survey,
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: 2011-04-03 11:01:26 UTC; wwal2
Repository: CRAN
Date/Publication: 2011-04-04 07:38:37

Package: ipw
Version: 1.0
Date: 2010-02-04
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: 2010-02-04 18:25:23 UTC; wwal
Repository: CRAN
Date/Publication: 2010-02-04 19:40:55

Title: Inverse Probability Weighted Cox Model with Corrected Sandwich
Author: Di Shu <shudi1991@gmail.com>, Rui Wang <rwang@hsph.harvard.edu>
Description: An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.

Title: Inverse Probability Weighting Estimation of Average Treatment
Author: Di Shu <shudi1991@gmail.com>, Grace Y. Yi <yyi@uwaterloo.ca>
Description: An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighting (IPW) estimation of average treatment effect (ATE) with misclassified outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method.