A modern idiomatic header-only 'C++'' interface for 'libhdf5'. Original software can be found at <https://github.com/highfive-devs/highfive/>.

Provide functionality to manage, clean and match highfrequency
    trades and quotes data, calculate various liquidity measures, estimate and
    forecast volatility, detect price jumps and investigate microstructure noise and intraday
    periodicity. A detailed vignette can be found in the open-access paper 
    "Analyzing Intraday Financial Data in R: The highfrequency Package" 
    by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>).

Syntax highlighter for R code based on the results of the R
    parser. Rendering in HTML and latex markup. Custom Sweave driver
    performing syntax highlighting of R code chunks.

Code Syntax Highlighting made easy for code snippets or complete
    files. Whether you're documenting your data analysis or creating interactive
    'shiny' apps.

A tool to format R markdown with CSS ids for HTML output. 
    The tool may be most helpful for those using markdown to create reproducible
    documents. The biggest limitations in formatting is the knowledge of CSS
    by the document authors.

Input multiple versions of a source document, 
    and receive HTML code for a highlighted version of the source document
    indicating the frequency of occurrence of phrases in the different versions.
    This method is described in Chapter 3 of 
    Rogers (2024) <https://digitalcommons.unl.edu/dissertations/AAI31240449/>.

A unified, flexible framework for high dimensional feature
    selection in the presence of a survival outcome. Provides multiple
    machine learning approaches (Cox elastic net, random survival forest,
    accelerated oblique random survival forest, gradient-boosted Cox,
    stability selection, classical univariate Cox screening, pseudo-
    observation bridging to arbitrary regression learners, and Fine-Gray
    competing risks selection) under a single interface. Adds causal
    survival forest estimation of heterogeneous treatment effects on
    survival (experimental), conformal survival prediction with finite-
    sample coverage guarantees, and time-dependent 'SHAP' explanations via
    'SurvSHAP(t)'. Methodology is based on regularised Cox regression
    (2011) <doi:10.18637/jss.v039.i05>, random survival forests (2008)
    <doi:10.1214/08-AOAS169>, oblique random survival forests (2024)
    <doi:10.1080/10618600.2023.2231048>, stability selection (2010)
    <doi:10.1111/j.1467-9868.2010.00740.x>, causal survival forests (2023)
    <doi:10.1111/rssb.12538>, time-dependent survival explanations (2023)
    <doi:10.1016/j.knosys.2022.110234>, conformal survival prediction (2023)
    <doi:10.1093/biomet/asad043>, the Fine-Gray model for competing
    risks (1999) <doi:10.1080/01621459.1999.10474144>,
    and pseudo-observation regression (2010)
    <doi:10.1177/0962280209105020>.