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

Implement the algorithm provided in scan for estimating the 
  transmission route on railway network using passenger volume. It is a 
  generalization of the scan statistic approach for railway network to identify
  the hot railway route for transmitting infectious diseases.

Estimates stop-signal reaction time (SSRT) in the stop-signal
    task using the integration and mean methods described by Verbruggen and
    colleagues (2019) <doi:10.7554/eLife.46323>. In addition to point
    estimates, the package provides Monte Carlo tools (nonparametric bootstrap
    confidence intervals, parametric ex-Gaussian simulation, minimum-trial-count
    and power analysis, and sensitivity analysis under violations of the
    horse-race assumptions) and Bayesian estimation via 'Stan', including
    single-subject and hierarchical ex-Gaussian horse-race models with an
    optional trigger-failure parameter following Matzke and colleagues (2013)
    <doi:10.1037/a0030543>, posterior inhibition functions, and posterior
    predictive checks. The Bayesian layer works with either the 'cmdstanr' or
    'rstan' backend.

Tools to import survey files in the '.sss' (triple-s) format. 
    The package provides the function 'read.sss()' that reads the '.asc' 
    (or '.csv') and '.sss' files of a triple-s survey data file. 
    See also <https://triple-s.org/>.

Implements sparse-smooth spatially varying coefficient quantile
    regression (SS-SVCQR), combining quantile regression of Koenker and
    Bassett (1978) <doi:10.2307/1913643>, grouped variable selection of Yuan
    and Lin (2006) <doi:10.1111/j.1467-9868.2005.00532.x>, graph
    regularization, and the alternating direction method of multipliers of
    Boyd et al. (2011) <doi:10.1561/2200000016>. The package provides
    graph-regularized estimation, spatially blocked cross-validation,
    prediction, diagnostics, and simulation helpers for global-local spatial
    quantile regression.