Title: Propensity Score Matching and Subclassification in Observational
person("Shu", "Yang", email = "shuyang@hsph.harvard.edu",
person("Brian G.", "Barkley", email = "BarkleyBG@outlook.com",
Description: Implements methods to estimate causal effects from observational
studies when there are 2+ distinct levels of treatment (i.e., "multilevel
treatment") using matching estimators, as introduced in Yang et al. (2016)
<doi:10.1111/biom.12505>. Matching on covariates, and matching or
stratification on modeled propensity scores, are available. These methods
require matching on only a scalar function of generalized propensity scores.
Brian G. Barkley [aut, cre] (<https://orcid.org/0000-0003-1787-4735>)

Title: Multilevel Propensity Score Analysis
Description: Functions to estimate and visualize propensity score
analysis for multilevel, or clustered, data.
Depends: ggplot2, plyr, psych, reshape, proto, grid, xtable, R (>=
Collate: 'geom_rug_alt.R' 'multilevelPSA-package.R' 'align.R' 'mlpsa.R'

Description: Functions to estimate and visualize propensity
score analysis for multilevel, or clustered, data.
Imports: PSAgraphics, plyr, psych, reshape, proto, grid, party

Imports: PSAgraphics, plyr, psych, reshape, proto, grid, party, MASS

Imports: PSAgraphics, plyr, psych, reshape, grid, party, MASS

Description: Conducts and visualizes propensity score analysis for multilevel,

Depends: ggplot2, plyr, psych, reshape, proto, grid, xtable, R (>= 3.0)

Title: Multilevel and Mixed Effects Model Diagnostics and Effect Sizes
Description: Effect sizes, diagnostics and performance metrics for
Includes marginal and conditional 'R2' estimates for linear mixed effects models
Imports: lme4, lmerTest, data.table (>= 1.12.0), nlme, extraoperators
(>= 0.1.1), JWileymisc (>= 1.1.0), cowplot, ggplot2, lavaan,

Title: Model Wrappers for Multi-Level Models
Description: Bindings for hierarchical regression models for use with the
'parsnip' package. Models include longitudinal generalized linear models
(Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and mixed-effect
Imports: dplyr, lme4, purrr, rlang, tibble, withr
Suggests: contrast, covr, gee, knitr, nlme, rmarkdown, spelling,