Palettes generated mostly from 'Wes Anderson' movies.

Implements a functional approximation of the four panel
    cointegration tests developed by Westerlund (2007)
    <doi:10.1111/j.1468-0084.2007.00477.x>. The tests are based on structural
    rather than residual dynamics and allow for heterogeneity in both the
    long-run cointegrating relationship and the short-run dynamics. The package
    includes logic for automated lag and lead selection via AIC/BIC, Bartlett 
    kernel long-run variance estimation, and a bootstrap procedure to handle 
    cross-sectional dependence. It also includes a bootstrapping distribution
    visualization function for diagnostic purposes.

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2019), <doi:10.1177/0962280218776989>). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.

Computes the exact observation weights for the Kalman filter and smoother, based on the method described in Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>.
    The package supports in-depth exploration of state-space models, enabling researchers and practitioners to extract meaningful insights from time series data.
    This functionality is especially valuable in dynamic factor models, where the computed weights can be used to decompose the contributions of individual variables to the latent factors.
    See the README file for examples.

A suite of routines for Weyl algebras.  Notation follows
   Coutinho (1995, ISBN 0-521-55119-6, "A Primer of Algebraic
   D-Modules").  Uses 'disordR' discipline
   (Hankin 2022 <doi:10.48550/arXiv.2210.03856>).  To cite
   the package in publications, use Hankin
   2022 <doi:10.48550/arXiv.2212.09230>.