Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference.

Estimates risk as a function of a marker by integrating over other covariates in a conditional risk model.

Provides the ability to perform "Marginal Mediation"--mediation
   wherein the indirect and direct effects are in terms of the average marginal effects 
   (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). 
   The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package.
   This framework allows the use of categorical mediators and outcomes with little change in interpretation
   from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details 
   on the method.

Multivariate ARIMA and ARIMA-X estimation using Spliid's 
    algorithm (marima()) and simulation (marima.sim()).

Miscellaneous functions and wrappers for development in other 
    packages created, maintained by Jordan Mark Barbone.

Render Markdown to full and lightweight HTML/LaTeX documents with
    the 'commonmark' package. It also supports features that are missing in
    'commonmark', such as raw HTML/LaTeX blocks, LaTeX math, superscripts,
    subscripts, footnotes, element attributes, appendices, and fenced 'Divs'.
    With additional JavaScript and CSS, it can also create HTML slides and
    articles.

An R-Shiny module containing a "markdownInput". This input allows
    the user to write some markdown code and to preview the result. This input
    has been inspired by the "comment" window of <https://github.com/>.

Functions for fitting various models to capture-recapture data
    including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and
    the multi-variate state model structure for survival
    estimation and POPAN structured Jolly-Seber models for abundance estimation.
    There are also Hidden Markov model (HMM) implementations of CJS and multistate
    models with and without state uncertainty and a simulation capability for HMM
    models.