Estimates correlation coefficients with associated
   confidence limits for bivariate, partially censored survival times. Uses
   the iterative multiple imputation approach proposed
   by Schemper, Kaider, Wakounig and Heinze (2013) <doi:10.1002/sim.5874>. Provides a scatterplot function to visualize the bivariate 
   distribution, either on the original time scale or as copula.

Plots survival models from the 'survival' package. Additionally, it
    plots curves of multistate models from the 'mstate' package. Typically, a
    plot is drawn by the sequence survplot(), confIntArea(), survCurve() and
    nrAtRisk(). The separation of the plot in this 4 functions allows for great
    flexibility to make a custom plot for publication.

Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables
  in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two
  survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival
  curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.

Provides deep learning models for right-censored survival data using the 'torch' backend. 
    Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, 
    and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, 
    hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, 
    and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.

Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).