Computes dynamical correlation estimates and percentile
        bootstrap confidence intervals for pairs of longitudinal
        responses, including consideration of lags and derivatives.

Provides a common interface for applying dimensionality reduction methods,
  such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps, 
  Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'), 
  and Uniform Manifold Approximation and Projection ('UMAP'). 
  Has built-in support for sparse matrices.

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>.