'Rapidjson' C++ Header Files

Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.

Polynomial Spline Routines

Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.

'Rapidxml' C++ Header Files

Provides XML parsing capability through the 'Rapidxml' 'C++' header-only library.

Fast Data Structures

Fast implementation of data structures, including a key-value store, stack, and queue. Environments are commonly used as key-value stores in R, but every time a new key is used, it is added to R's global symbol table, causing a small amount of memory leakage. This can be problematic in cases where many different keys are used. Fastmap avoids this memory leak issue by implementing the map using data structures in C++.

Fast 'JSON', 'NDJSON' and 'GeoJSON' Parser and Generator

A fast 'JSON' parser, generator and validator which converts 'JSON', 'NDJSON' (Newline Delimited 'JSON') and 'GeoJSON' (Geographic 'JSON') data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from 'JSON'. 'GeoJSON' data is read in as 'simple features' objects. This implementation wraps the 'yyjson' 'C' library which is available from < https://github.com/ibireme/yyjson>.

Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit

Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) ). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA< https://portal.gdc.cancer.gov/> and GEO< https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) for more details.

Stan Models for Item Response Theory

Streamlines the fitting of common Bayesian item response models using Stan.

Integrated Code Chunk Anchoring and Referencing for R Markdown Documents

A streamlined cross-referencing system for R Markdown documents generated with 'knitr'. R Markdown is an authoring format for generating dynamic content from R. 'kfigr' provides a hook for anchoring code chunks and a function to cross-reference document elements generated from said chunks, e.g. figures and tables.

Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) ; Bürkner (2018) ; Bürkner (2021) ; Carpenter et al. (2017) .

XPtr Add-Ons for 'Rcpp'

Provides the means to compile user-supplied C++ functions with 'Rcpp' and retrieve an 'XPtr' that can be passed to other C++ components.