A personalized dynamic latent factor model (Zhang et al. (2024) <doi:10.1093/biomet/asae015>) for irregular multi-resolution time series data, to interpolate unsampled measurements from low-resolution time series.

Incremental Multiple Correspondence Analysis and Principal
    Component Analysis.

Interpreting the differences between mean scale scores across various
    forms of an assessment can be challenging. This difficulty arises from different
    mappings between raw scores and scale scores, complex mathematical relationships,
    adjustments based on judgmental procedures, and diverse equating functions applied
    to different assessment forms. An alternative method involves running simulations
    to explore the effect of incrementing raw scores on mean scale scores. The
    'idmact' package provides an implementation of this approach based on the
    algorithm detailed in Schiel (1998)
    <https://www.act.org/content/dam/act/unsecured/documents/ACT_RR98-01.pdf> which
    was developed to help interpret differences between mean scale scores on the
    American College Testing (ACT) assessment. The function idmact_subj() within
    the package offers a framework for running simulations on subject-level scores.
    In contrast, the idmact_comp() function provides a framework for conducting
    simulations on composite scores.

Utilities to work with data from the Internal Displacement
    Monitoring Centre (IDMC) (<https://www.internal-displacement.org/>),
    with convenient functions for loading events data from the IDMC API
    and transforming events data to daily displacement estimates.

Provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".

Contains techniques for mining large and high-dimensional data
    sets by using the concept of Intrinsic Dimension (ID). Here the ID is
    not necessarily an integer. It is extended to fractal dimensions. And
    the Morisita estimator is used for the ID estimation, but other
    tools are included as well.