Palettes generated mostly from 'Wes Anderson' movies.

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>.

Provides a computationally efficient way of fitting
	     weighted linear fixed effects estimators for causal
	     inference with various weighting schemes. Weighted linear
	     fixed effects estimators can be used to estimate the
	     average treatment effects under different identification
	     strategies. This includes stratified randomized
	     experiments, matching and stratification for
	     observational studies, first differencing, and
	     difference-in-differences. The package implements methods
	     described in Imai and Kim (2017) "When should We Use
	     Linear Fixed Effects Regression Models for Causal
	     Inference with Longitudinal Data?", available at
	     <https://imai.fas.harvard.edu/research/FEmatch.html>.