Package: ewp 0.1.2
ewp: An Empirical Model for Underdispersed Count Data
Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) <doi:10.1191/1471082X04st064oa>.
Authors:
ewp_0.1.2.tar.gz
ewp_0.1.2.zip(r-4.7-x86_64)ewp_0.1.2.zip(r-4.7-arm64)ewp_0.1.2.zip(r-4.6-x86_64)ewp_0.1.2.zip(r-4.6-arm64)ewp_0.1.2.zip(r-4.5-x86_64)
ewp_0.1.2.tgz(r-4.6-x86_64)ewp_0.1.2.tgz(r-4.6-arm64)ewp_0.1.2.tgz(r-4.5-x86_64)ewp_0.1.2.tgz(r-4.5-arm64)
ewp_0.1.2.tar.gz(r-4.7-arm64)ewp_0.1.2.tar.gz(r-4.7-x86_64)ewp_0.1.2.tar.gz(r-4.6-arm64)ewp_0.1.2.tar.gz(r-4.6-x86_64)
ewp_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ewp/json (API)
| # Install 'ewp' in R: |
| install.packages('ewp', repos = c('https://pboesu.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pboesu/ewp/issues
- linnet - Linnet clutch sizes
Last updated from:c926fe1f64. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 120 | ||
| linux-devel-x86_64 | OK | 156 | ||
| source / vignettes | OK | 254 | ||
| linux-release-arm64 | OK | 117 | ||
| linux-release-x86_64 | OK | 193 | ||
| macos-release-arm64 | OK | 124 | ||
| macos-release-x86_64 | OK | 257 | ||
| macos-oldrel-arm64 | OK | 121 | ||
| macos-oldrel-x86_64 | OK | 312 | ||
| windows-devel-arm64 | OK | 364 | ||
| windows-devel-x86_64 | OK | 156 | ||
| windows-release-arm64 | OK | 365 | ||
| windows-release-x86_64 | OK | 97 | ||
| windows-oldrel-x86_64 | OK | 164 | ||
| wasm-release | OK | 115 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract coefficients | coef.ewp |
| Probability mass function of the three-parameter EWP | dewp3 |
| Probability mass function of the three-parameter EWP | dewp3_cpp |
| Exponentially weighted Poisson regression model | ewp_reg |
| Extract fitted values | fitted.ewp |
| Linnet clutch sizes | linnet |
| Extract log likelihood | logLik.ewp |
| Estimate marginal means | mmean |
| Predict from fitted model | predict.ewp |
| Print ewp model object | print.ewp |
| Print ewp model summary | print.summary.ewp |
| Random samples from the three-parameter EWP | rewp3 |
| simulate from fitted model | simulate.ewp |
| Model summary | summary.ewp |
| Extract estimated variance-covariance matrix | vcov.ewp |