Tools for working with temporal discounting data, designed for behavioural researchers to simplify data cleaning/scoring and model fitting. The package implements widely used methods such as computing indifference points from adjusting amount task (Frye et al., 2016, <doi:10.3791/53584>), testing for non-systematic discounting per the criteria of Johnson & Bickel (2008, <doi:10.1037/1064-1297.16.3.264>), scoring questionnaires according to the methods of Kirby et al. (1999, <doi:10.1037//0096-3445.128.1.78>) and Wileyto et al (2004, <doi:10.3758/BF03195548>), Bayesian model selection using a range of discount functions (Franck et al., 2015, <doi:10.1002/jeab.128>), drift diffusion models of discounting (Peters & D'Esposito, 2020, <doi:10.1371/journal.pcbi.1007615>), and model-agnostic measures of discounting such as area under the curve (Myerson et al., 2001, <doi:10.1901/jeab.2001.76-235>) and ED50 (Yoon & Higgins, 2008, <doi:10.1016/j.drugalcdep.2007.12.011>).

Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.

Implements the Temporal Forest algorithm for feature selection in 
    high-dimensional longitudinal data. The method combines time-aware network 
    construction via weighted gene co-expression network analysis (WGCNA), module-based feature screening, and stability 
    selection using tree-based models. This package provides tools for 
    reproducible longitudinal analysis, closely following the methodology 
    described in Shao, Moore, and Ramirez (2025) <https://github.com/SisiShao/TemporalForest>.

Provides native R implementations of the multiphase parametric
    hazard model of Blackstone, Naftel, and Turner (1986)
    <doi:10.1080/01621459.1986.10478314> with a focus on behavioral parity,
    transparent numerics, and reproducible validation against reference
    outputs from the original 'C'/'SAS' HAZARD program, originally developed at
    the University of Alabama at Birmingham (UAB). The 'SAS'/'C' code and this R
    package are currently developed and maintained at The Cleveland Clinic
    Foundation, and the R code was wholly developed at The Cleveland Clinic
    Foundation. The generalized temporal
    decomposition family extends to longitudinal mixed-effects settings
    (Rajeswaran et al. 2018 <doi:10.1177/0962280215623583>). The package is
    intentionally implemented in pure R first; performance-critical paths may
    later be accelerated with 'Rcpp' without changing the public interface.