Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) <doi:10.1111/2041-210X.14038>.

It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 <doi:10.1080/03610926.2019.1570266>). Such methods, developed within a Bayesian framework through Stan <https://mc-stan.org/>, come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 <doi:10.18637/jss.v108.i01>).

Implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.

Implementation of Testlet Item Response Theory (tirt). 
    A light-version yet comprehensive and streamlined framework for psychometric analysis using 
    unidimensional 
    Item Response Theory (IRT; Baker & Kim (2004) <doi:10.1201/9781482276725>) and 
    Testlet Response Theory (TRT; Wainer et al., (2007) <doi:10.1017/CBO9780511618765>). 
    Designed for researchers, this package supports the estimation of item and person 
    parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) 
    and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration.
    Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint 
    Maximum Likelihood, the package offers robust tools for scale linking and 
    equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability 
    across mixed-format test forms. It also facilitates fixed-parameter calibration, 
    enabling users to estimate person abilities with known item parameters or 
    vice versa, which is essential for pre-equating studies and item bank 
    maintenance. Comprehensive data simulation functions are included to generate 
    synthetic datasets with complex structures, including mixed-model blocks and 
    specific testlet effects, aiding in methodological research and study design 
    validation. Researchers can try multiple simulation situations.