Performs likelihood-based inference for stationary time series 
    extremes.  The general approach follows Fawcett and Walshaw (2012)
    <doi:10.1002/env.2133>.  Marginal extreme value inferences are adjusted for 
    cluster dependence in the data using the methodology in Chandler and Bate 
    (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood 
    for the model parameters.  A log-likelihood for the extremal index is 
    produced using the K-gaps model of Suveges and Davison (2010) 
    <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make 
    inferences about extreme values. Both maximum likelihood and Bayesian 
    approaches are available.

Temporary and permanent message queues for R. Built on top of
    'SQLite' databases. 'SQLite' provides locking, and makes it possible
    to detect crashed consumers. Crashed jobs can be automatically marked
    as "failed", or put in the queue again, potentially a limited number of times.

An updated implementation of R package 'ranger' by Wright et al,
    (2017) <doi:10.18637/jss.v077.i01> for training and predicting from random
    forests, particularly suited to high-dimensional data, and for embedding in
    'Multiple Imputation by Chained Equations' (MICE) by van Buuren (2007)
    <doi:10.1177/0962280206074463>. Ensembles of classification and regression
    trees are currently supported. Sparse data of class 'dgCMatrix' (R package
    'Matrix') can be directly analyzed. Conventional bagged predictions are
    available alongside an efficient prediction for MICE via the algorithm
    proposed by Doove et al (2014) <doi:10.1016/j.csda.2013.10.025>. Survival
    and probability forests are not supported in the update, nor is data of
    class 'gwaa.data' (R package 'GenABEL'); use the original 'ranger' package
    for these analyses.

Dataset and functions to explore quality of literary novels. The package is a part of the Riddle of Literary Quality project, and it contains the data of a reader survey about fiction in Dutch, a description of the novels the readers rated, and the results of stylistic measurements of the novels. The package also contains functions to combine, analyze, and visualize these data. For more details, see: Eder M, van Zundert J, Lensink S, van Dalen-Oskam K (2022). Replicating The Riddle of Literary Quality: The litRiddle package for R. In _Digital Humanities 2022: Conference Abstracts_, 636-637.