Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).

Functions to access Twitter's filter, sample, and user streams, and to
    parse the output into data frames.

A collection of functions to search and download street view imagery 
             ('Mapilary' <https://www.mapillary.com/developer/api-documentation>) and 
             to extract, quantify, and visualize visual features. Moreover, there are 
             functions provided to generate Qualtrics survey in TXT format using 
             the collection of street views for various research purposes.

It contains functions to estimate multivariate Student's t dynamic and static regression models for given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form.
    Poudyal, N., and Spanos, A. (2022) <doi:10.3390/econometrics10020017>.
    Spanos, A. (1994) <http://www.jstor.org/stable/3532870>.

The stress addition approach is an alternative to the traditional
    concentration addition or effect addition models. It allows the modelling
    of tri-phasic concentration-response relationships either as single toxicant
    experiments, in combination with an environmental stressor or as mixtures of
    two toxicants. See Liess et al. (2019) <doi:10.1038/s41598-019-51645-4>
    and Liess et al. (2020) <doi:10.1186/s12302-020-00394-7>.

Traditional model evaluation metrics fail to capture model 
    performance under less than ideal conditions. This package employs 
    techniques to evaluate models "under-stress". This includes testing 
    models' extrapolation ability, or testing accuracy on specific 
    sub-samples of the overall model space. Details describing stress-testing 
    methods in this package are provided in 
    Haycock (2023) <doi:10.26076/2am5-9f67>. The other primary contribution of
    this package is provided to R users access to the 'Python' library 'PyCaret'
    <https://pycaret.org/> for quick and easy access to auto-tuned 
    machine learning models.