sptwdglm
R-package for performing Bayesian Variable Selection in Double Generalized Linear Tweedie Spatial Process Models
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: wiley.com -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.9%) to scientific vocabulary
Keywords
Repository
R-package for performing Bayesian Variable Selection in Double Generalized Linear Tweedie Spatial Process Models
Basic Info
- Host: GitHub
- Owner: arh926
- License: other
- Language: R
- Default Branch: master
- Homepage: https://doi.org/10.51387/23-NEJSDS37
- Size: 169 KB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
sptwdglm: An R-package for performing Bayesian Variable selection using Spike and Slab priors for Double Generalized Linear Tweedie Spatial Process Models
<!-- badges: end -->
sptwdglm contains MCMC algorithms for fitting the following models:
Function | Models
:---- | :-------------
dglm.autograd.R | Double Generalzied Linear Model (DGLM)
ssdglm.autograd.R | Spike and Slab Priors for DGLMs
spdglm.autograd.R | Spatial DGLMs
spssdglm.autograd.R | Spike and Slab Priors for Spatial DGLMs
Variable selection is performed using the function FDR.R on the model coefficients.
It supplements the paper titled, "Bayesian Variable Selection in Double Generalized Linear Tweedie Spatial Process Models", New England Journal of Statistics in Data Science: Special Issue on Modern Bayesian Methods with Applications in Data Science (https://doi.org/10.51387/23-NEJSDS37).
All of the above MCMC samplers use a Metropolis Adjusted Langevin Algorithm (MALA, see Girolami and Calderhead, 2011 https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2010.00765.x).
Figure showing spatial patterns on the left column and logarithm of aggregated Tweedie response on the right column for 10,000 realizations across 100 locations.
Installation
You can install the development version of sptwdglm like so:
``` r
if you dont have devtools installed
install.packages("devtools")
devtools::install_github("arh926/sptwdglm") ```
Example
There are examples contained within every function. Please install the package to view them.
``` r require(sptwdglm)
non-spatial
mc <- Function(response, mean covariates, dispersion covariates, mcmc parameters)
spatial
mc <- Function(coordinates, response, mean covariates, dispersion covariates, mcmc parameters)
Diagnostics
plot_mcmc(posterior samples)
Variable selection through FDR for coefficients
FDR(mean coefficients) FDR(dispersion coefficients) ```
Owner
- Name: Aritra Halder
- Login: arh926
- Kind: user
- Location: Philadelphia, PA
- Company: Drexel University
- Website: https://sites.google.com/view/aritra-halder/home
- Twitter: ahalder926
- Repositories: 8
- Profile: https://github.com/arh926
Assistant Professor of Biostatistics
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Halder" given-names: "Aritra" orcid: "https://orcid.org/0000-0002-5139-3620" title: "sptwdglm" version: 1.0.0 doi: date-released: 2023-04-10 url: "https://github.com/arh926/sptwdglm"
GitHub Events
Total
- Push event: 1
Last Year
- Push event: 1
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Aritra Halder | a****6@g****m | 49 |
Issues and Pull Requests
Last synced: about 2 years ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0