Provides density functions for the joint distribution of
    choice, response time and confidence for discrete confidence judgments
    as well as functions for parameter fitting, prediction and simulation
    for various dynamical models of decision confidence.  All models are
    explained in detail by Hellmann et al.  (2023;
    Preprint available at <https://osf.io/9jfqr/>, published version: <doi:10.1037/rev0000411>).  Implemented models are the dynaViTE model, 
    dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, <doi:10.1037/a0019737>),
    and various race models.  C++ code for dynWEV and 2DSD is based on the
    'rtdists' package by Henrik Singmann.

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

Fits Bayesian state-space models for non-Gaussian time series using a latent log-rate (Poisson) or
    latent logit (binomial) formulation. The latent trajectory follows a
    first-order random walk or a stationary AR(1) process, sampled by
    Metropolis-within-Gibbs using the implied Gaussian Markov random field (GMRF) full conditionals. Four innovation
    structures are supported for the latent increments: constant-variance
    Gaussian, Student-t, a finite scale mixture of normals, and stochastic
    volatility. Both families support time-constant zero inflation. The
    package provides simulation, fitting, forecasting, summary and plotting
    tools. It implements and extends the methodology of Zens and Bijak (2026)
    <doi:10.1214/26-AOAS2171>.

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.