Description: Data from the six Australian Federal Elections (House of
Representatives) between 2001 and 2016, and from the four Australian
Censuses over the same period. Includes tools for visualizing and
analysing the data, as well as imputing Census data for years in
which a Census does not occur. This package incorporates
Imports: dplyr, shiny, ggplot2, ggthemes, magrittr, rgeos, plotly (>=
4.5.6), sp, tidyr, graphics, stats, purrr, colourpicker, rgdal,
Suggests: testthat, knitr, rmarkdown, maptools, purrrlyr, GGally,
corrplot, broom, scales, readr, gridExtra, tidyverse, tibble,
person("Jeremy", "Forbes", role = c("aut", "cre"), email = "jeremyforbes1995@gmail.com"),
person("Ben", "Marwick",   role = c("aut")),
person("Commonwealth of Australia", "AEC", role = c("cph")),
person("Australian Bureau of Statistics", "ABS", role = c("cph")))
Australian Bureau of Statistics ABS [cph]

Description: Data from the seven Australian Federal Elections (House of
Representatives) between 2001 and 2019, and from the four Australian
Electoral Commission and Australian Bureau of Statistics) 2019.
corrplot, broom, scales, readr, gridExtra, tidyverse, spelling

corrplot, broom, scales, readr, gridExtra, tidyverse, spelling,
Authors@R: c(person("Jeremy", "Forbes", role = c("aut", "cre"), email = "jeforbes14@gmail.com"),
person("Di", "Cook",       role = c("aut")),

Title: Robust Analytical Methods for Evaluating Educational
Interventions using Randomised Controlled Trials Designs
Description: Analysing data from evaluations of educational interventions using a randomised controlled trial design. Various analytical tools to perform sensitivity analysis using different methods are supported (e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The included commands can be used for simple randomised trials, cluster randomised trials and multisite trials. The methods can also be used more widely beyond education trials. This package can be used to evaluate other intervention designs using Frequentist and Bayesian multilevel models.
Author: Germaine Uwimpuhwe, Akansha Singh, Steve Higgins, ZhiMin Xiao, Ewoud De Troyer and Adetayo Kasim
Imports: lme4, mvtnorm, graphics, stats, rstanarm, ggplot2, methods