Streamlines data preprocessing, analysis, and visualization for association rule mining. Designed to work with the 'arules' package, features include discretizing data frames, generating rule set intersections, and visualizing rules with heatmaps and Euler diagrams. 'RulesTools' also includes a dataset on Brook trout detection from Nolan et al. (2022) <doi:10.1007/s13412-022-00800-x>.

This holds r markdown and quarto templates for academic papers and 
    slide decks. It also has templates to create research projects which 
    contain academic papers as vignettes.

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.

Doubly ranked tests are nonparametric tests for grouped functional and multivariate data.
    The testing procedure first ranks a matrix (or three dimensional array) of data by column (if a matrix) or by cell (across the third dimension if an array).
    By default, it calculates a sufficient statistic for the subject's order within the sample using the observed ranks, taken over the columns or cells.
    Depending on the number of groups, G, the summarized ranks are then analyzed using either a Wilcoxon Rank Sum test (G = 2) or a Kruskal-Wallis (G greater than 2).