The parametric Bayes analysis for the 
    restricted mean survival time (RMST) with cluster effect,
    as described in Hanada and Kojima (2024) <doi:10.48550/arXiv.2406.06071>.
    Bayes estimation with random-effect and frailty-effect can be applied to 
    several parametric models useful in survival time analysis.
    The RMST under these parametric models can be computed from 
    the obtained posterior samples.

Performs two-sample comparisons using the restricted mean survival time (RMST) when survival curves end at different time points between groups. This package implements a sensitivity approach that allows the threshold  timepoint tau to be specified after the longest survival time in the shorter survival group. Two kinds of between-group contrast estimators (the difference in RMST and the ratio of RMST) are computed: Uno et al(2014)<doi:10.1200/JCO.2014.55.2208>, Uno et al(2022)<https://CRAN.R-project.org/package=survRM2>, Ueno and Morita(2023)<doi:10.1007/s43441-022-00484-z>.

Tools for Restricted Mean Survival Time based study design and 
    analysis planning. Provides power and sample size calculations for two-arm studies using direct 
    modeling approaches from the literature, including
    semiparametric additive models, 
    linear Inverse Probability Weighting based models 
    from Wei (2014) <doi:10.1093/biostatistics/kxt050>, 
    multiplicative stratified models from 
    Wang (2019) <doi:10.1002/sim.8356>, and
    covariate-dependent censoring methods from 
    Wang (2018) <doi:10.1007/s10985-017-9391-6>.

Contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment.

We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model.