Copyright: copyright (c) OpenReliability.org 2011-2019

Description: Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986).

Title: Accelerated Life Testing Using 'WeibullR'
Description: Graphical data analysis of accelerated life tests. Methods derived from  Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6).
Copyright: copyright (c) OpenReliability.org 2022
Depends: R (>= 3.5.0), WeibullR, methods

Title: Weighting for Covariate Balance in Observational Studies
Description: Generates weights to form equivalent groups in observational studies by easing and extending the functionality of the R packages 'twang' (Ridgeway et al., 2017) <https://CRAN.R-project.org/package=twang> for generalized boosted modeling, 'CBPS' (Fong, Ratkovic, & Imai, 2017) <https://CRAN.R-project.org/package=CBPS> for covariate balancing propensity score weighting, 'ebal' (Hainbueller, 2014) <https://CRAN.R-project.org/package=ebal> for entropy balancing, and 'ATE' (Haris & Chan, 2015) <https://CRAN.R-project.org/package=ATE> for empirical balancing calibration weighting. Also allows for assessment of weights and checking of covariate balance by interfacing directly with 'cobalt' (Greifer, 2017) <https://CRAN.R-project.org/package=cobalt>.
Packaged: 2017-10-17 02:32:08 UTC; NoahGreifer

Description: Generates weights to form equivalent groups in observational studies with point or longitudinal treatments by easing and extending the functionality of the R packages 'twang' for generalized boosted modeling (McCaffrey, Ridgeway & Morral, 2004) <doi:10.1037/1082-989X.9.4.403>, 'CBPS' for covariate balancing propensity score weighting (Imai & Ratkovic, 2014) <doi:10.1111/rssb.12027>, 'ebal' for entropy balancing (Hainmueller, 2012) <doi:10.1093/pan/mpr025>, 'optweight' for optimization-based weights (Zubizarreta, 2015) <doi:10.1080/01621459.2015.1023805>, 'ATE' for empirical balancing calibration weighting (Chan, Yam, & Zhang, 2016) <doi:10.1111/rssb.12129>, and 'SuperLearner' for stacked machine learning-based propensity scores (Pirracchio, Petersen, & van der Laan, 2015) <doi:10.1093/aje/kwu253>. Also allows for assessment of weights and checking of covariate balance by interfacing directly with 'cobalt'.
Imports: cobalt (>= 4.2.2), ggplot2 (>= 3.0.0), crayon, backports (>=
MASS, gbm (>= 2.1.3), misaem (>= 1.0.0), knitr, rmarkdown
Packaged: 2020-07-07 08:25:53 UTC; NoahGreifer
Author: Noah Greifer [aut, cre] (<https://orcid.org/0000-0003-3067-7154>)