Title: Confidence or Prediction Intervals, Quantiles, and Probabilities
Authors@R: c(person("John", "Haman", role = c("cre", "aut"),
Description: Functions to append confidence intervals, prediction intervals,
and other quantities of interest to data frames. All appended quantities
are for the response variable, after conditioning on the model and covariates.
This package has a data frame first syntax that allows for easy piping.
Currently supported models include (log-) linear, (log-) linear mixed,
Imports: arm, dplyr, lme4, magrittr, merTools, methods, stats, tibble

person("Matthew", "Avery", role = "aut",
Imports: arm, boot, dplyr, lme4, magrittr, MASS, stats, tibble, utils

Imports: arm, boot, dplyr, lme4, magrittr, MASS, methods, stats,
Suggests: broom, knitr, rmarkdown, tidyverse

generalized linear models, generalized linear mixed models, and

Suggests: broom, here, knitr, rmarkdown, SPREDA, tidyverse

Imports: arm, boot, dplyr, lme4, MASS, methods, stats, survival, utils
Suggests: here, ggplot2, knitr, rmarkdown, SPREDA

Title: Causal Inference in Case-Control Studies
person("Sung Jae", "Jun", email = "suj14@psu.edu", role = "aut"),
person("Sokbae", "Lee", email = "sl3841@columbia.edu", role = c("aut", "cre")))
Description: Estimation and inference methods for causal relative risk.
Semiparametrically efficient estimation of the aggregated (log) odds ratio and a causal inference procedure for relative risk.
For more details, see the paper by Jun and Lee (2020), "Causal Inference in Case-Control Studies," <arXiv:2004.08318 [econ.EM]>.
Packaged: 2020-09-11 09:14:05 UTC; sokbaelee

Description: Estimation and inference methods for causal relative and attributable risk in case-control and case-population studies.
Semiparametrically efficient estimation of the aggregated (log) odds ratio and causal inference procedures for relative and attributable risk.
Suggests: knitr, rmarkdown, testthat, MASS
Packaged: 2020-10-29 09:44:04 UTC; sokbaelee

Title: Provide Tours of 'Shiny' Applications
Description: Provide step by step guided tours of 'Shiny' applications.