Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from 'lme4pureR' (Walker and Bates (2013) <https://github.com/lme4/lme4pureR>).

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. This package can be used with any test dataset on which you have computed prior probabilities of case status, posterior probabilities of case status, and you have the observed case-control status.  In comparison with the C-statistic (area under ROC curve), the expected weight of evidence (expected information for discrimination) has several advantages as a summary measure of predictive performance. 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.