Title: Logrank Test for Aggregated Survival Data
Author: Jonas Richter-Dumke and Roland Rau
Description: LogrankA provides a logrank test across unlimited groups with the
possibility to input aggregated survival data.

Depends: R (>= 2.15.0), survival, survminer
Description: Power of the log-rank test is estimated using simulation datasets, with user specified total sample size (in one simulation dataset), type I error, effect size, the total number of simulation datasets, sample size ratio between two comparison groups, the death rate in the reference group, and the distribution of follow-up time (simulated from a negative binomial distribution). Method reference: Hogg, R. V., McKean, J., and Craig, A. T. (2004, ISBN 10: 0130085073).

Title: Recursive Partitioning for Longitudinal Profiles Using Baseline
Maintainer: Madan G Kundu <madan_g.kundu@yahoo.com>
Description: Constructs longitudinal tree (i.e., regression tree with heterogeneous longitudinal profile) for continuous longitudinal outcome using baseline covariates as partitioning variables according to the 'LongCART' algorithm as described in Kundu and Harezlak (2019) <doi:10.1080/24709360.2018.1557797>.

Title: Recursive Partitioning for Longitudinal Data and Right Censored
Depends: R (>= 3.4.0), nlme, rpart, survival, magic, survminer
Description: Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively.

Description: Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.

Title: A Tool for 'Confounder'-Sensitive Longitudinal Analysis on
Description: This tool takes longitudinal dataset as input and analyzes if there is significant
change of the features over time (a proxy for treatments), while detects and controls
for 'confounders' simultaneously. 'LongDat' is able to take in several data types as input,
including count, proportion, binary, ordinal and continuous data. The output table contains
p values, effect sizes and 'confounders' of each feature, making the downstream analysis easy.
Imports: lme4, reshape2, glmmTMB, emmeans, bestNormalize, MASS,
ggplot2, stringr, magrittr, tibble, dplyr, graphics, utils,
stats, rlang, car, rstatix, effsize, tidyr, patchwork
Suggests: rmarkdown, knitr, tidyverse, kableExtra
Author: Chia-Yu Chen [aut, cre] (<https://orcid.org/0000-0003-1765-7132>),