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

Accessible and flexible implementation of three ecoacoustic indices that are less commonly available in existing R frameworks: Background Noise, Soundscape Power and Soundscape Saturation. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2014) <doi:10.1016/j.procs.2014.05.063> and Burivalova (2017) <doi:10.1111/cobi.12968>.

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.

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>.

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>.