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>.

A (not yet exhaustive) collection of common models of risk
    processes in actuarial science, represented as formal S4 classes. Each class
    (risk model) has a simulator of its path, and a plotting function. Further, 
    a Monte-Carlo estimator of a ruin probability for a finite time is
    implemented, using a parallel computation. Currently, the package extends
    two classical risk models Cramer-Lundberg and Sparre Andersen models by
    including capital injections, that are positive jumps (see Breuer L. and
    Badescu A.L. (2014) <doi:10.1080/03461238.2011.636969>). The intent of the
    package is to provide a user-friendly interface for ruin processes'
    simulators, as well as a solid and extensible structure for future
    extensions.