Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.

Shiny-App that allows to annotate vectors of texts to predefined categories by hand.

Converts among many citation formats, including 'BibTeX',
    'Citeproc', 'Codemeta', 'RDF XML', 'RIS', 'Schema.org', and
    'Citation File Format'. A low level 'R6' class is provided, as well
    as stand-alone functions for each citation format for both read
    and write.

An S4 implementation of Eq. (3) and Eq. (7) by
    David J. Hand and Robert J. Till (2001) <DOI:10.1023/A:1010920819831>.

Perform statistical writership analysis of scanned handwritten documents.
             Webpage provided at: <https://github.com/CSAFE-ISU/handwriter>.

Perform statistical writership analysis of scanned handwritten documents with a 'shiny' app for 'handwriter'.

Perform forensic handwriting analysis of two scanned handwritten documents. This package implements the statistical method described by Madeline Johnson and Danica Ommen (2021) <doi:10.1002/sam.11566>. Similarity measures and a random forest produce a score-based likelihood ratio that quantifies the strength of the evidence in favor of the documents being written by the same writer or different writers.

Several handy plots for quickly looking at the relationship between two numeric vectors of equal length. Quickly visualize scatter plots, residual plots, qq-plots, box plots, confidence intervals, and prediction intervals.

Builds and optimizes Hopfield artificial neural networks (Hopfield, 1982, <doi:10.1073/pnas.79.8.2554>). One-layer and three-layer models are implemented. The energy of the Hopfield network is minimized with formula from Krotov and Hopfield (2016, <doi:10.48550/ARXIV.1606.01164>). Optimization (supervised learning) is done through a gradient-based method. Classification is done with S3 methods predict(). Parallelization with 'OpenMP' is used if available during compilation.