Title: Panel Smooth Transition Regression Modelling
Authors@R: person("Yukai", "Yang", email = "yukai.yang@statistik.uu.se",role = c("aut", "cre"))
Description: Provides the Panel Smooth Transition Regression (PSTR) modelling.
The modelling procedure consists of three stages: Specification, Estimation and Evaluation.
The package offers sharp tools helping the package user(s) to conduct model specification tests,
to do PSTR model estimation, and to do model evaluation.
The tests implemented in the package allow for cluster-dependency and are heteroskedasticity-consistent.
The wild bootstrap and wild cluster bootstrap tests are also implemented.
Parallel computation (as an option) is implemented in some functions, especially the bootstrap tests.
The package suits tasks running many cores on super-computation servers.

Parallel computation (as an option) is implemented in some functions for bootstrap tests.

Imports: tibble, snowfall, ggplot2, plotly, stats, magrittr

Author: Yukai Yang [aut, cre] (<https://orcid.org/0000-0002-2623-8549>)

Title: Propensity Score Weighting Methods for Dichotomous Treatments
Description: Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) doubly robust estimation of treatment effect. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.
Author: Huzhang Mao <Huzhang.Mao@uth.tmc.edu>, Liang Li <LLi15@mdanderson.org>