A strong type system for R which supports
    symbol declaration and assignment with type checking
    and condition checking.

An R interface to the 'typeform'
    <https://typeform.com> application program interface.  Also provides
    functions for downloading your results.

Implements Bayesian inference for the conditional genetic 
    stock identification model.  It allows inference of mixed fisheries and also 
    simulation of mixtures to predict accuracy.  A full description of the underlying
    methods is available in a recently published article in the
    Canadian Journal of Fisheries and Aquatic Sciences: <doi:10.1139/cjfas-2018-0016>.

Unobserved Components Models (introduced in Harvey, A. (1989),
    Forecasting, structural time series models and the Kalman filter, Cambridge
    New York: Cambridge University Press) decomposes a time series into
    components such as trend, seasonal, cycle, and the regression effects due
    to predictor series which captures the salient features of the series to
    predict its behavior.

R bindings for functions from the UCR Suite by Rakthanmanon et al. (2012) <DOI:10.1145/2339530.2339576>, which enables ultrafast subsequence
      search for a best match under Dynamic Time Warping and Euclidean Distance.

ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

The real-time quantitative polymerase chain reaction
    (qPCR) technical data sets by Ruijter et al. (2013)
    <doi:10.1016/j.ymeth.2012.08.011>: (i) the four-point 10-fold dilution
    series; (ii) 380 replicates; and (iii) the competimer data set. These
    three data sets can be used to benchmark qPCR methods. Original data set
    is available at
    <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>.
    This package fixes incorrect annotations in the original data sets.

Wraps the 'StarSpace' library <https://github.com/facebookresearch/StarSpace> 
    allowing users to calculate word, sentence, article, document, webpage, link and entity 'embeddings'. 
    By using the 'embeddings', you can perform text based multi-label classification, 
    find similarities between texts and categories, do collaborative-filtering based recommendation 
    as well as content-based recommendation, find out relations between entities, calculate 
    graph 'embeddings' as well as perform semi-supervised learning and multi-task learning on plain text. 
    The techniques are explained in detail in the paper: 'StarSpace: Embed All The Things!' by Wu et al. (2017), available at <arXiv:1709.03856>.