Abstract
The BPP package contains R code to perform Bayesian projection pursuit regression under various regression settings. Bayesian projection pursuit regression learns the functional relationship between inputs and outputs by considering a sum of functions of one dimensional projections of the inputs. This is useful as a general machine learning software.
- Developers:
- Release Date:
- 2022-07-29
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC52-06NA25396
- Code ID:
- 78008
- Site Accession Number:
- C21079
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Country of Origin:
- United States
Citation Formats
Francom, Devin, Collins, Gavin, and Rumsey, Kelin.
BPP R package.
Computer Software.
https://github.com/gqcollins/BayesPPR.
USDOE Laboratory Directed Research and Development (LDRD) Program.
29 Jul. 2022.
Web.
doi:10.11578/dc.20220729.8.
Francom, Devin, Collins, Gavin, & Rumsey, Kelin.
(2022, July 29).
BPP R package.
[Computer software].
https://github.com/gqcollins/BayesPPR.
https://doi.org/10.11578/dc.20220729.8.
Francom, Devin, Collins, Gavin, and Rumsey, Kelin.
"BPP R package." Computer software.
July 29, 2022.
https://github.com/gqcollins/BayesPPR.
https://doi.org/10.11578/dc.20220729.8.
@misc{
doecode_78008,
title = {BPP R package},
author = {Francom, Devin and Collins, Gavin and Rumsey, Kelin},
abstractNote = {The BPP package contains R code to perform Bayesian projection pursuit regression under various regression settings. Bayesian projection pursuit regression learns the functional relationship between inputs and outputs by considering a sum of functions of one dimensional projections of the inputs. This is useful as a general machine learning software.},
doi = {10.11578/dc.20220729.8},
url = {https://doi.org/10.11578/dc.20220729.8},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20220729.8}},
year = {2022},
month = {jul}
}