A two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.

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.

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.