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.

Tools for creating data validation pipelines and
    tidy reports. This package offers a framework for exploring and
    validating data frame like objects using 'dplyr' grammar of data
    manipulation.

Bindings for additional models for use with the 'parsnip'
    package.  Models include prediction rule ensembles (Friedman and
    Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992
    ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013)
    <doi:10.1007/978-1-4614-6849-3>.

This holds some r markdown and quarto templates and a template to 
    create a research project in "R Studio".

Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.