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clusttmb

spatial finite mixture model R package

https://github.com/andrea-havron/clusttmb

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    2 of 7 committers (28.6%) from academic institutions
  • ○
    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.3%) to scientific vocabulary

Keywords from Contributors

standardization interpretability autograder stock-synthesis pde imputation anova correlation animal hack
Last synced: 11 months ago · JSON representation

Repository

spatial finite mixture model R package

Basic Info
Statistics
  • Stars: 5
  • Watchers: 5
  • Forks: 1
  • Open Issues: 7
  • Releases: 0
Created about 5 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Changelog Contributing

README.Rmd 

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# clustTMB


[![R-CMD-check](https://github.com/Andrea-Havron/clustTMB/workflows/call-r-cmd-check/badge.svg)](https://github.com/Andrea-Havron/clustTMB/actions)
[![codecov](https://codecov.io/gh/Andrea-Havron/clustTMB/branch/main/graph/badge.svg?token=kfHYcSdj88)](https://app.codecov.io/gh/Andrea-Havron/clustTMB)


clustTMB fits finite mixture models that incorporate both covariate and random effect
structure within either the gating, the expert, or both components of the model. The
clustTMB model is built in Template Model Builder (TMB) and implemented in R using formula
syntax similar to other finite mixture modeling packages such as mclust. The TMB structure
of the finite mixture model allows for fast computations of random effects by using the
Laplace Approximation to calculate the marginal likelihood of fixed effects after integrating
out random components. Spatial random effects are estimated through approximations of
the Gaussian Markov Random Fields via the FEM-SPDE method introduced by INLA.

## Installation

You can install the development version from [GitHub](https://github.com/) with:

```{r install clustTMB, eval = FALSE}
remotes::install_github("Andrea-Havron/clustTMB", build_vignettes = TRUE)
```

## Example

A basic example is illustrated with the faithful dataset:
```{r print-example, eval = FALSE}
library(clustTMB)
data("faithful")
m1 <- clustTMB(response = faithful, covariance.structure = "VVV")
```


```{r run-example, echo = FALSE, message=FALSE, warning=FALSE}
library(clustTMB)
data("faithful")
m1 <- clustTMB(response = faithful, covariance.structure = "VVV")
plot(faithful$eruptions, faithful$waiting, pch = 16, col = m1$report$classification + 1, xlab = "eruptions", ylab = "waiting")
legend(4.2, 57.5, legend = c("Class 1", "Class 2"), col = c("black", "red"), pch = 16)
```

## Vignettes

For more detailed examples, see vignettes:

- [Available Covariance Structures](https://andrea-havron.github.io/clustTMB/articles/CovarianceStructure.html)
- [Spatial Example](https://andrea-havron.github.io/clustTMB/articles/SpatialExMeuseData.html)

```{r vignettes, eval = FALSE}
## Not Run

# install.packages("utils")
utils::browseVignettes("clustTMB")

## End Not Run
```

Owner

  • Login: Andrea-Havron
  • Kind: user

GitHub Events

Total
  • Pull request event: 1
  • Fork event: 1
Last Year
  • Pull request event: 1
  • Fork event: 1

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 283
  • Total Committers: 7
  • Avg Commits per committer: 40.429
  • Development Distribution Score (DDS): 0.615
Past Year
  • Commits: 90
  • Committers: 5
  • Avg Commits per committer: 18.0
  • Development Distribution Score (DDS): 0.156
Top Committers
Name Email Commits
Andrea-Havron a****n@g****m 109
Andrea-Havron-NOAA a****n@n****v 93
Andrea-Havron a****n@g****m 66
GitHub Actions a****s@g****m 9
Andrea Havron a****n@u****u 3
Andrea-Havron-NOAA 8****A 2
github-actions[bot] 4****] 1
Committer Domains (Top 20 + Academic)
uw.edu: 1 github.com: 1 noaa.gov: 1

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 13
  • Total pull requests: 23
  • Average time to close issues: 3 days
  • Average time to close pull requests: about 2 months
  • Total issue authors: 2
  • Total pull request authors: 4
  • Average comments per issue: 0.85
  • Average comments per pull request: 0.0
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 11
Past Year
  • Issues: 1
  • Pull requests: 6
  • Average time to close issues: about 5 hours
  • Average time to close pull requests: 7 days
  • Issue authors: 1
  • Pull request authors: 3
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 1
View more stats
Top Authors
Issue Authors
  • Andrea-Havron-NOAA (7)
  • Andrea-Havron (4)
Pull Request Authors
  • github-actions[bot] (12)
  • Andrea-Havron-NOAA (12)
  • k-doering-NOAA (2)
  • Andrea-Havron (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 164 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
cran.r-project.org: clustTMB

Spatio-Temporal Finite Mixture Model using 'TMB'

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 164 Last month
Rankings
Dependent packages count: 28.0%
Dependent repos count: 34.6%
Average: 49.8%
Downloads: 86.8%
Maintainers (1)
andrea.havron@noaa.gov
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.0.0 depends
  • Matrix * imports
  • MoEClust * imports
  • TMB >= 1.8.1 imports
  • clustMixType * imports
  • cluster * imports
  • glmmTMB * imports
  • lme4 * imports
  • mclust * imports
  • methods * imports
  • stats * imports
  • GGally * suggests
  • INLA >= 21.2.23 suggests
  • covr * suggests
  • cowplot * suggests
  • ggplot2 * suggests
  • ggspatial * suggests
  • giscoR * suggests
  • inlabru * suggests
  • kableExtra * suggests
  • knitr * suggests
  • mvnfast * suggests
  • rmarkdown * suggests
  • sf * suggests
  • sp * suggests
  • splancs * suggests
  • testthat * suggests
  • tweedie * suggests
  • wesanderson * suggests
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