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rcppcore
Rcpp:Seamless R and C++ Integration
The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <https://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
Maintained by Dirk Eddelbuettel. Last updated 2 days ago.
c-plus-plusc-plus-plus-11c-plus-plus-14c-plus-plus-17c-plus-plus-20rcppcpp
755 stars 22.63 score 11k scripts 13k dependents
stan-dev
rstan:R Interface to Stan
User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Maintained by Ben Goodrich. Last updated 5 days ago.
bayesian-data-analysisbayesian-inferencebayesian-statisticsmcmcstancpp
1.1k stars 18.86 score 14k scripts 281 dependents