| Package Name | Access | Summary | Updated |
|---|---|---|---|
| r-viridis | public | Port of the new 'matplotlib' color maps ('viridis' - the default -, 'magma', 'plasma' and 'inferno') to 'R'. 'matplotlib' <http://matplotlib.org/ > is a popular plotting library for 'python'. These color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness. | 2025-03-25 |
| r-plotly | public | Easily translate ggplot2 graphs to an interactive web-based version and/or create custom web-based visualizations directly from R. Once uploaded to a plotly account, plotly graphs (and the data behind them) can be viewed and modified in a web browser. | 2025-03-25 |
| r-stanheaders | public | The C++ header files of the Stan project are provided by this package, but it contains no R code, shared objects, vignettes, or function documentation. It is only useful for developers who want to utilize the LinkingTo directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo and (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, templated statistical and linear algebra functions that can handle the automatically differentiable scalar types (and doubles, ints, etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models. | 2025-03-25 |
| r-inline | public | Functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions. | 2025-03-25 |
| r-ggplot2 | public | An implementation of the grammar of graphics in R. It combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, and you can still build up a plot step by step from multiple data sources. It also implements a sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes. See http://ggplot2.org for more information, documentation and examples. | 2025-03-25 |
