Automatically creates separate regression models for different spatial 
    regions. The prediction surface is smoothed using a regional border smoothing 
    method. If regional models are continuous, the resulting prediction surface is 
    continuous across the spatial dimensions, even at region borders. Methodology 
    is described in Wagstaff and Bean (2023) <doi:10.32614/RJ-2023-004>.

A small wrapper on 'regexpr' to extract the matches and
    captured groups from the match of a regular expression to a character
    vector.

Wrappers on 'regexpr' and 'gregexpr' to return the match
    results in tidy data frames.

Remove the background from an image using pre-trained deep
    learning segmentation models ('U-2-Net', 'ISNet', 'BiRefNet' and others)
    run through the 'ONNX' Runtime via the 'onnxr' package. Given an image, a
    model predicts a foreground alpha matte which is composited into a cutout
    with a transparent (or solid-colour) background; optional closed-form alpha
    matting (ported from 'pymatting') refines soft edges. An R port of the
    Python 'rembg' package (<https://github.com/danielgatis/rembg>). Models are
    downloaded on first use and cached in a per-user cache directory.

Empirical and simulated data for relational event analyses. 
    Each dataset consists of a relational event sequence and optional actor attributes. 
    Individual datasets are redistributed under their original licenses 
    as documented in inst/DATA_LICENSES.

An 'RStudio' addin providing shortcuts for writing in 'Markdown'. This package provides a series of 
             functions that allow the user to be more efficient when using 'Markdown'. For example, you can select 
             a word, and put it in bold or in italics, or change the alignment of elements inside you Rmd. The idea
             is to map all the functionalities from 'remedy' on keyboard shortcuts, so that it provides an interface 
             close to what you can find in any other text editor.

Methods and tools for implementing regularized multivariate functional principal component analysis ('ReMFPCA') for multivariate functional data whose variables might be observed over different dimensional domains. 'ReMFPCA' is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, 'ReMFPCA' generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in 'ReMFPCA', please refer to Haghbin et al. (2023) <doi:10.48550/arXiv.2306.13980>.