Data | Introduction | Example workflow | Step 1: Estimate the point process parameters | Step 2: Train a mark model | Step 3: Check model fit | Step 4: Simulate from the fitted model | Summary
Package: ldmppr 1.1.3.9000
ldmppr: Estimate and Simulate from Location Dependent Marked Point Processes
A suite of tools for estimating, assessing model fit, simulating from, and visualizing location dependent marked point processes characterized by regularity in the pattern. You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model. 'ldmppr' estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools. Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results. Documentation for the package 'ldmppr' is available in the form of a vignette.
Authors:
ldmppr_1.1.3.9000.tar.gz
ldmppr_1.1.3.9000.zip(r-4.7-x86_64)ldmppr_1.1.3.9000.zip(r-4.7-arm64)ldmppr_1.1.3.9000.zip(r-4.6-x86_64)ldmppr_1.1.3.9000.zip(r-4.6-arm64)ldmppr_1.1.3.9000.zip(r-4.5-x86_64)
ldmppr_1.1.3.9000.tgz(r-4.6-x86_64)ldmppr_1.1.3.9000.tgz(r-4.6-arm64)ldmppr_1.1.3.9000.tgz(r-4.5-x86_64)ldmppr_1.1.3.9000.tgz(r-4.5-arm64)
ldmppr_1.1.3.9000.tar.gz(r-4.7-arm64)ldmppr_1.1.3.9000.tar.gz(r-4.7-x86_64)ldmppr_1.1.3.9000.tar.gz(r-4.6-arm64)ldmppr_1.1.3.9000.tar.gz(r-4.6-x86_64)
ldmppr_1.1.3.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ldmppr/json (API)
| # Install 'ldmppr' in R: |
| install.packages('ldmppr', repos = c('https://lanedrew.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/lanedrew/ldmppr/issues
Pkgdown/docs site:https://lanedrew.github.io
Datasets:
- medium_example_data - Medium Example Data
- small_example_data - Small Example Data
Last updated from:1e6b5c4357. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 197 | ||
| linux-devel-x86_64 | OK | 208 | ||
| source / vignettes | OK | 313 | ||
| linux-release-arm64 | OK | 201 | ||
| linux-release-x86_64 | OK | 193 | ||
| macos-release-arm64 | OK | 394 | ||
| macos-release-x86_64 | OK | 357 | ||
| macos-oldrel-arm64 | OK | 172 | ||
| macos-oldrel-x86_64 | OK | 361 | ||
| windows-devel-arm64 | OK | 422 | ||
| windows-devel-x86_64 | OK | 197 | ||
| windows-release-arm64 | OK | 424 | ||
| windows-release-x86_64 | OK | 148 | ||
| windows-oldrel-x86_64 | OK | 159 | ||
| wasm-release | OK | 192 |
Exports:%>%as_nloptrcheck_model_fitestimate_process_parametersextract_covarsgenerate_mppldmppr_budgetsldmppr_gridsldmppr_mark_modelload_mark_modelmpp.ldmppr_simplot_mpppower_law_mappingpredict_markssave_mark_modelscale_rasterssimulate_mppsimulate_sctrain_mark_model
Dependencies:abindbase64encbslibbundlecachemclasscliclockclustercodetoolscpp11crayondata.tabledeldirdiagramdialsDiceDesigndigestdoParalleldplyrevaluatefarverfastmapfontawesomeforeachfsfurrrfuturefuture.applyGauProgenericsGETggplot2globalsgluegoftestgowergridExtragtablehardhathighrhmshtmltoolsipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalbfgslifecyclelistenvlubridatemagrittrMASSMatrixmemoisemimemixoptmodelenvnlmenloptrnnetnumDerivparallellyparsnippillarpkgconfigpolyclipprettyunitsprodlimprogressprogressrpurrrR6rappdirsRColorBrewerRcppRcppArmadillorecipesrlangrmarkdownrpartrsampleS7sassscalessfdshapeslidersparsevctrsspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilssplitfngrSQUAREMstringistringrsurvivaltailortensorterratibbletidyrtidyselecttimechangetimeDatetinytextunetzdbutf8vctrsviridisLitewarpwithrworkflowsxfunxgboostyamlyardstick
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Check the fit of an estimated model using global envelope tests | check_model_fit |
| Estimate point process parameters using log-likelihood maximization | estimate_process_parameters |
| Extract covariate values from a set of rasters | extract_covars |
| Generate a marked process given locations and marks | generate_mpp |
| Create an optimization budget specification for estimate_process_parameters() | ldmppr_budgets |
| Optimization budget specification object | as.data.frame.ldmppr_budgets as.list.ldmppr_budgets ldmppr_budgets-class length.ldmppr_budgets print.ldmppr_budgets print.summary.ldmppr_budgets summary.ldmppr_budgets [.ldmppr_budgets |
| Fitted point-process model object | as.data.frame.ldmppr_fit as_nloptr as_nloptr.ldmppr_fit coef.ldmppr_fit ldmppr_fit logLik.ldmppr_fit nobs.ldmppr_fit plot.ldmppr_fit print.ldmppr_fit print.summary.ldmppr_fit summary.ldmppr_fit |
| Create a grid schedule for estimate_process_parameters() | ldmppr_grids |
| Grid schedule object | as.data.frame.ldmppr_grids as.list.ldmppr_grids ldmppr_grids-class length.ldmppr_grids print.ldmppr_grids print.summary.ldmppr_grids summary.ldmppr_grids [.ldmppr_grids |
| Mark model object | ldmppr_mark_model load_mark_model predict.ldmppr_mark_model print.ldmppr_mark_model print.summary.ldmppr_mark_model save_mark_model save_mark_model.ldmppr_mark_model summary.ldmppr_mark_model |
| Model fit diagnostic object | ldmppr_model_check plot.ldmppr_model_check print.ldmppr_model_check print.summary.ldmppr_model_check summary.ldmppr_model_check |
| Simulated marked point process object | as.data.frame.ldmppr_sim ldmppr_sim mpp.ldmppr_sim nobs.ldmppr_sim plot.ldmppr_sim print.ldmppr_sim print.summary.ldmppr_sim summary.ldmppr_sim |
| Medium Example Data | medium_example_data |
| Plot a marked point process | plot_mpp |
| Gentle decay (power-law) mapping function from sizes to arrival times | power_law_mapping |
| Predict values from the mark distribution | predict_marks |
| Scale a set of rasters | scale_rasters |
| Simulate a realization of a location dependent marked point process | simulate_mpp |
| Simulate from the self-correcting model | simulate_sc |
| Small Example Data | small_example_data |
| calculates acceptance for thinning mechanism during simulation | thin_st_fast |
| Train a flexible model for the mark distribution | train_mark_model |