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.

Exploration of pharmacometrics data involves both general tools (transformation and plotting) and specific techniques (non-compartmental analysis). This kind of exploration is generally accomplished by utilizing different packages. The purpose of 'ruminate' is to create a 'shiny' interface to make these tools more broadly available while creating reproducible results.

Plots multiple run charts, finds successive signals of 
    improvement, and revises medians when each signal occurs. Finds runs
    above, below, or on both sides of the median, and returns a plot and
    a data.table summarising original medians and any revisions, for all
    groups within the supplied data.