Constrained clustering, transfer functions, and other methods for analysing Quaternary science data.

Turns regression models inside out. Functions decompose variances and coefficients for various regression model types. Functions also visualize regression model objects using techniques developed in Schoon, Melamed, and Breiger (2024) <doi:10.1017/9781108887205>.

Utilities to access Integrated Food Security Phase Classification
    (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are
    available for all of the 'IPC-CH' Public API (<https://docs.api.ipcinfo.org>)
    simplified and advanced endpoints to easily download the data in a clean and
    tidy format.

Ports the Ripser <doi:10.48550/arXiv.1908.02518>
    and Cubical Ripser <doi:10.48550/arXiv.2005.12692>
    persistent homology calculation engines from C++.
    Can be used as a rapid calculation tool in topological data analysis
    pipelines.

The open sourced data management software 'Integrated
    Rule-Oriented Data System' ('iRODS') offers solutions for the whole
    data life cycle (<https://irods.org/>). The loosely constructed and
    highly configurable architecture of 'iRODS' frees the user from strict
    formatting constraints and single-vendor solutions. This package
    provides an interface to the 'iRODS' HTTP API, allowing you to manage
    your data and metadata in 'iRODS' with R. Storage of annotated files
    and R objects in 'iRODS' ensures findability, accessibility,
    interoperability, and reusability of data.

Parameter estimation, computation of probability, information, and 
    (log-)likelihood, and visualization of item/test characteristic curves and
    item/test information functions for three uni-dimensional item response theory
    models: the 3-parameter-logistic model, generalized partial credit model, 
    and graded response model. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rirt>.

Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data.