Functions to estimate start and duration of moult from moult 
             data, based on models developed in Underhill 
             and Zucchini (1988, 1990).

Lattice functions for drawing folded empirical cumulative
    distribution plots, or mountain plots. A mountain plot is similar
    to an empirical CDF plot, except that the curve increases from
    0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
    the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.

Helping psychologists and other behavioural scientists
    to analyze mouse movement (and other 2-D trajectory) data. Bundles
    together several functions that compute spatial measures (e.g., maximum
    absolute deviation, area under the curve, sample entropy) or provide a
    shorthand for procedures that are frequently used (e.g., time 
    normalization, linear interpolation, extracting initiation and movement 
    times). For more information on these dependent measures, see Wirth et al. 
    (2020) <doi:10.3758/s13428-020-01409-0>.

Mouse-tracking, the analysis of mouse movements in computerized
    experiments, is a method that is becoming increasingly popular in the
    cognitive sciences. The mousetrap package offers functions for importing,
    preprocessing, analyzing, aggregating, and visualizing mouse-tracking data.
    An introduction into mouse-tracking analyses using mousetrap can be found
    in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2023)
    <doi:10.31234/osf.io/v685r> (preprint: 
    <https://osf.io/preprints/psyarxiv/v685r>).

Provides methods for detecting multivariate outliers in numeric datasets. The package implements classical Mahalanobis distance, robust Minimum Covariance Determinant (MCD), and Principal Component Analysis (PCA)-based approaches for outlier detection. The methodology is informed by Aggarwal (2017) <doi:10.1007/978-3-319-47578-3> and Grentzelos, Caroni and Barranco-Chamorro (2020) <doi:10.1002/cmm4.1129>. Visualization functions are included to aid interpretation of detected outliers. Mahalanobis distance calculations are accelerated using 'C++' through 'Rcpp'.