Performs emulation of dynamic simulators using Gaussian process via one-step ahead approach. The package implements a flexible framework for approximating time-dependent outputs from computationally expensive dynamic systems. It is specifically designed for nonlinear dynamic systems where full simulations may be costly. The underlying Gaussian process model accounts for temporal dependency through the one-step-ahead formulation, allowing for accurate emulation of complex dynamics. Hyperparameters are estimated via maximum likelihood. For methodological details, see Heo (2025, <doi:10.48550/arXiv.2503.20250>) for exact method, and Mohammadi, Challenor, and Goodfellow (2019, <doi:10.1016/j.csda.2019.05.006>) for Monte Carlo method.

An anonymization algorithm to resist neighbor label attack in a dynamic network.

Based on random forest principle, 'DynForest' is able to include 
    multiple longitudinal predictors to provide individual predictions. 
    Longitudinal predictors are modeled through the random forest. The 
    methodology is fully described for a survival outcome in: 
    Devaux, Helmer, Genuer & Proust-Lima (2023) 
    <doi: 10.1177/09622802231206477>.

A novel, multi-modal simulation engine for
    studying dynamic cellular processes at single-cell resolution. 'dyngen'
    is more flexible than current single-cell simulation engines. It
    allows better method development and benchmarking, thereby stimulating
    development and testing of novel computational methods. Cannoodt et
    al. (2021) <doi:10.1038/s41467-021-24152-2>.

Dynamic linear models and time series regression.

Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) <doi:10.1016/j.apm.2013.05.038>; Nagy et al. (2011) <doi:10.1002/acs.1239>.

Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, and mgcv::gam model objects.