A client that grants access to the power of the 'ohsome API'
    from R. It lets you analyze the rich data source of the 
    'OpenStreetMap (OSM)' history. You can retrieve the geometry of 'OSM' 
    data at specific points in time, and you can get aggregated statistics 
    on the evolution of 'OSM' elements and specify your own temporal, 
    spatial and/or thematic filters.

Facilitates the automatic detection of acoustic signals, 
    providing functions to diagnose and optimize the performance of detection 
    routines. Detections from other software can also be explored and optimized. 
    This package has been peer-reviewed by rOpenSci.
    Araya-Salas et al. (2022) <doi:10.1101/2022.12.13.520253>.

Provides simple crosstab output with optional statistics (e.g., Goodman-Kruskal Gamma, Somers' d, and Kendall's tau-b) as well as two-way and one-way tables. The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data.

Crawler for 'OJS' pages and scraper for meta-data from articles. 
    You can crawl 'OJS' archives, issues, articles, galleys, and search results. 
    You can scrape articles metadata from their head tag in html, 
    or from Open Archives Initiative ('OAI') records.
    Most of these functions rely on 'OJS' routing conventions 
    (<https://docs.pkp.sfu.ca/dev/documentation/en/architecture-routes>).

A collection of aesthetically appealing color palettes for effective data visualization with 'ggplot2'. Palettes support both discrete and continuous data.

Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) <DOI:10.1109/ACCESS.2020.3010099>.