Title: Sakai Sequential Relation Analysis
Authors@R: c(person(given="Takuya", family="Yanagida", email="takuya.yanagida@univie.ac.at", role=c("cre","aut")),
Description: Takeya Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA)
for polytomous items for examining whether each pair of items has a sequential or equal
relation. Package includes functions for generating a sequential relation table and a
treegram to visualize sequential or equal relations between pairs of items.

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

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

Authors@R: person("Anton", "Leontyev", email = "a.g.leontiev@tamu.edu", comment = c(ORCID = "0000-0001-8880-6161"),  role = c("aut", "cre"))
Description: 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>.
Author: Anton Leontyev [aut, cre] (<https://orcid.org/0000-0001-8880-6161>)

Description: This package is for using R as a server side scripting

language with session, application and cookie support

Description: A package for writing server side R scripts with session,
X-CRAN-Comment: Orphaned on 2011-12-22 as requested by the original
maintainer, Mehmet Hakan Satman <mhsatman@gmail.com>.

Description: This package is using R as a server side scripting

Title: Functions for Stochastic Search Variable Selection (SSVS)
Description: 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
Imports: bayestestR, BoomSpikeSlab, checkmate, ggplot2, graphics,
Suggests: AER, testthat (>= 3.0.0), knitr, rmarkdown