An R port of the margins command from 'Stata', which can be used to
    calculate marginal (or partial) effects from model objects.

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

Analyzing longitudinal clinical data from Electronic Health Records (EHRs) using linear mixed models (LMM) and visualizing the results as networks. It includes functions for fitting LMM, normalizing adjacency matrices, and comparing networks. The package is designed for researchers in clinical and biomedical fields who need to model longitudinal data and explore relationships between variables For more details see Bates et al. (2015) <doi:10.18637/jss.v067.i01>.

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. This package has been superseded by 'litedown'.

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.

Implementation of the 'MarkerPen' algorithm, short for marker gene detection
    via penalized principal component analysis, described in the paper by Qiu, Wang, Lei,
    and Roeder (2021, <doi:10.1093/bioinformatics/btab257>). 'MarkerPen' is a semi-supervised
    algorithm for detecting marker genes by combining prior marker information with bulk
    transcriptome data.