A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).

Implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies <University of Chicago Press> and Farine & Whitehead (2015) <doi: 10.1111/1365-2656.12418>. In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.

A collection of functions for computing centrographic
        statistics (e.g., standard distance, standard deviation
        ellipse, standard deviation box) for observations taken at
        point locations. Separate plotting functions have been
        developed for each measure. Users interested in writing results
        to ESRI shapefiles can do so by using results from 'aspace'
        functions as inputs to the convert.to.shapefile() and
        write.shapefile() functions in the 'shapefiles' library. We intend to
        provide 'terra' integration for geographic data in a future release.
        The 'aspace' package was originally conceived to aid in the analysis of
        spatial patterns of travel behaviour (see Buliung and Remmel 2008
        <doi:10.1007/s10109-008-0063-7>).

Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.