Sample Size Calculation using Restricted Mean Survival Time
Calculates the power and sample size based on the difference in Restricted Mean Survival Times.

Calculates the power and sample size based on the difference in Restricted Mean Survival Time.

person("Anton", "Leontyev", email = "a.g.leontiev@tamu.edu", comment = c(ORCID = "0000-0001-8880-6161"),  role = c("aut", "cre"))
This is a collection of functions to calculate stop-signal reaction time (SSRT). Includes functions for both "integration" and "mean" methods; both fixed and adaptive stop-signal delays are supported (see appropriate functions). Calculation is based on Verbruggen et al. (2019) <doi:10.7554/eLife.46323.001> and Verbruggen et al. (2013) <doi:10.1177/0956797612457390>.
Anton Leontyev [aut, cre] (<https://orcid.org/0000-0001-8880-6161>)

This package is for using R as a server side scripting
language

This package is for using R as a server side scripting
language with session, application and cookie support

A package for writing server side R scripts with session,
application and cookie support
Orphaned on 2011-12-22 as requested by the original
maintainer, Mehmet Hakan Satman <mhsatman@gmail.com>.

This package is using R as a server side scripting
language

Functions for Stochastic Search Variable Selection (SSVS)
c(person(given="Sierra",
family="Bainter",
role=c("cre", "aut"),
email="sbainter@miami.edu"),
person(given="Thomas",
family="McCauley",
role=c("aut"),
email="thomasgranvillemccauley@gmail.com"),
person(given="Mahmoud",
family="Fahmy",
role=c("aut"),
email="mahmoud.m.fahm@gmail.com"),
person("Dean", "Attali",
role = "aut",
email = "daattali@gmail.com",
comment= c(ORCID="0000-0002-5645-3493")))
Functions for performing stochastic search variable selection (SSVS)
for binary and continuous outcomes and visualizing the results.
SSVS is a Bayesian variable selection method used to estimate the probability
that individual predictors should be included in a regression model.
Using MCMC estimation, the method samples thousands of regression models
in order to characterize the model uncertainty regarding both the predictor
set and the regression parameters.
bayestestR, BoomSpikeSlab, checkmate, ggplot2, graphics,
rlang, stats
AER, testthat (>= 3.0.0), knitr, rmarkdown
Sierra Bainter [cre, aut],
Thomas McCauley [aut],
Mahmoud Fahmy [aut],
Dean Attali [aut] (<https://orcid.org/0000-0002-5645-3493>)