| Type: | Package |
| Title: | Your Go-to Motif Accountant |
| Version: | 0.2.1 |
| Depends: | R (≥ 4.1.0) |
| Description: | Provides the 'C++' header-only library 'barry' for use in R packages. 'barry' is a 'C++' template library for counting sufficient statistics on binary arrays and building discrete exponential-family models. It provides tools for sparse arrays, user-defined count statistics, support set constraints, power set generation, and includes modules for Discrete Exponential Family Models (DEFMs) and network statistics. By placing these headers in this package, we offer an efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. This package follows the same approach as the 'BH' package which provides 'Boost' headers for R packages. |
| URL: | https://github.com/USCbiostats/barryr, https://uscbiostats.github.io/barryr/ |
| BugReports: | https://github.com/USCbiostats/barryr/issues |
| License: | MIT + file LICENSE |
| RoxygenNote: | 7.3.3 |
| Encoding: | UTF-8 |
| NeedsCompilation: | no |
| Packaged: | 2025-11-24 20:51:09 UTC; runner |
| Author: | George Vega Yon 
[aut, cre] |
| Maintainer: | George Vega Yon <g.vegayon@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-12-01 13:50:02 UTC |
barry: 'C++' Headers for the 'barry' Library
Description
Provides 'C++' header-only files for the 'barry' library, which is a template library for counting sufficient statistics on binary arrays and building discrete exponential-family models. The 'barry' library includes tools for sparse arrays, user-defined count statistics, support set constraints, power set generation, and modules for Discrete Exponential Family Models (DEFMs) and network statistics.
Details
This package follows the same approach as the 'BH' package which provides
'Boost' headers for R. To use 'barry' in your R package, add LinkingTo: barry
to your DESCRIPTION file. The headers will then be available for inclusion
in your 'C++' code via #include <barry/barry.hpp>.
The 'barry' library was created by Dr. George G. Vega Yon as part of his doctoral dissertation and provides a general framework for building discrete exponential-family models, particularly useful for Exponential Random Graph Models (ERGMs) and other network statistics.
Author(s)
Maintainer: George Vega Yon g.vegayon@gmail.com (ORCID)
See Also
Useful links: