A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) <doi:10.18637/jss.v105.i05>. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.

Bootstrap, permutation tests, and jackknife,
	featuring easy-to-use syntax.

Package of data sets from "Mathematical Statistics
    with Resampling in R" (1st Ed. 2011, 2nd Ed. 2018) by Laura Chihara and Tim Hesterberg.

Data sets for Chihara and Hesterberg (2022, ISBN: 978-1-119-87404-1)
    "Mathematical Statistics with Resampling in R" (3rd Ed).

Maximum likelihood estimation for univariate reducible
 stochastic differential equation models. Discrete, possibly noisy
 observations, not necessarily evenly spaced in time. Can fit
 multiple individuals/units with global and local parameters, by
 fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019)
 "Estimating reducible stochastic differential equations by
 conversion to a least-squares problem", Computational Statistics
 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.

Functions for dissimilarity analysis and memory-based learning 
    (MBL, a.k.a local modeling) in complex spectral data sets. 
    Most of these functions are based on the methods presented in 
    Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.

Measure single-storage water supply system performance using resilience,
    reliability, and vulnerability metrics; assess storage-yield-reliability
    relationships; determine no-fail storage with sequent peak analysis; optimize
    release decisions for water supply, hydropower, and multi-objective reservoirs
    using deterministic and stochastic dynamic programming; generate inflow
    replicates using parametric and non-parametric models; evaluate inflow
    persistence using the Hurst coefficient.