Package: detect 0.5-0
detect: Analyzing Wildlife Data with Detection Error
Models for analyzing site occupancy and count data models with detection error, including single-visit based models (Lele et al. 2012 <doi:10.1093/jpe/rtr042>, Moreno et al. 2010 <doi:10.1890/09-1073.1>, Solymos et al. 2012 <doi:10.1002/env.1149>, Denes et al. 2016 <doi:10.1111/1365-2664.12818>), conditional distance sampling and time-removal models (Solymos et al. 2013 <doi:10.1111/2041-210X.12106>, Solymos et al. 2018 <doi:10.1650/CONDOR-18-32.1>). Package development was supported by the Alberta Biodiversity Monitoring Institute and the Boreal Avian Modelling Project.
Authors:
detect_0.5-0.tar.gz
detect_0.5-0.zip(r-4.5)detect_0.5-0.zip(r-4.4)detect_0.5-0.zip(r-4.3)
detect_0.5-0.tgz(r-4.4-any)detect_0.5-0.tgz(r-4.3-any)
detect_0.5-0.tar.gz(r-4.5-noble)detect_0.5-0.tar.gz(r-4.4-noble)
detect_0.5-0.tgz(r-4.4-emscripten)detect_0.5-0.tgz(r-4.3-emscripten)
detect.pdf |detect.html✨
detect/json (API)
| # Install 'detect' in R: |
| install.packages('detect', repos = c('https://psolymos.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/psolymos/detect/issues
Last updated 7 months agofrom:9e5fd779e3. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 22 2024 |
| R-4.5-win | OK | Dec 22 2024 |
| R-4.5-linux | OK | Dec 22 2024 |
| R-4.4-win | OK | Dec 22 2024 |
| R-4.4-mac | OK | Dec 22 2024 |
| R-4.3-win | OK | Dec 22 2024 |
| R-4.3-mac | OK | Dec 22 2024 |
Exports:AUCbootstrapcmulticmulti.fitcmulti.fit0cmulti2.fitconvertEDRdrop.scope.svisitextractBOOTextractMLEhbootindexis.presentload_BAM_QPADpredictMCMCrocplotsvabusvabu.fitsvabu.stepsvoccsvocc.fitsvocc.stepzif
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Analyzing Wildlife Data with Detection Error | detect-package detect |
| AUC ROC plot for fitted models | AUC rocplot |
| Do bootstrap and extract bootstrap results | bootstrap extractBOOT |
| Conditional Multinomial Maximum Likelihood Estimation | cmulti cmulti.fit cmulti2.fit fitted.cmulti model.frame.cmulti model.matrix.cmulti predict.cmulti |
| Conversion between truncated and unlimited effective detection distance (EDR) | convertEDR |
| Simulated example for abundance model | databu |
| Simulated example for occupancy model | datocc |
| Hierarchical bootstrap indices | hbootindex |
| Load BAM QPAD parameter estimates and support functions | load_BAM_QPAD |
| Ovenbird abundances | oven |
| Single visit N-mixture abundance models | is.present predictMCMC svabu svabu.fit svabu.step svabu_nb.fit zif |
| ZI Binomial model with single visit | extractMLE svocc svocc.fit svocc.step |
