Package: ldmppr 1.0.3.9000
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_1.0.3.9000.tar.gz
ldmppr_1.0.3.9000.zip(r-4.5)ldmppr_1.0.3.9000.zip(r-4.4)ldmppr_1.0.3.9000.zip(r-4.3)
ldmppr_1.0.3.9000.tgz(r-4.4-x86_64)ldmppr_1.0.3.9000.tgz(r-4.4-arm64)ldmppr_1.0.3.9000.tgz(r-4.3-x86_64)ldmppr_1.0.3.9000.tgz(r-4.3-arm64)
ldmppr_1.0.3.9000.tar.gz(r-4.5-noble)ldmppr_1.0.3.9000.tar.gz(r-4.4-noble)
ldmppr_1.0.3.9000.tgz(r-4.4-emscripten)ldmppr_1.0.3.9000.tgz(r-4.3-emscripten)
ldmppr.pdf |ldmppr.html✨
ldmppr/json (API)
NEWS
| # 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
- small_example_data - Small Example Data
Last updated 20 days agofrom:3564949f13. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 03 2024 |
| R-4.5-win-x86_64 | OK | Dec 03 2024 |
| R-4.5-linux-x86_64 | OK | Dec 03 2024 |
| R-4.4-win-x86_64 | OK | Dec 03 2024 |
| R-4.4-mac-x86_64 | OK | Dec 03 2024 |
| R-4.4-mac-aarch64 | OK | Dec 03 2024 |
| R-4.3-win-x86_64 | OK | Dec 03 2024 |
| R-4.3-mac-x86_64 | OK | Dec 03 2024 |
| R-4.3-mac-aarch64 | OK | Dec 03 2024 |
Exports:%>%C_theta2_icheck_model_fitconditional_sumconditional_sum_logicaldist_one_dimestimate_parameters_scestimate_parameters_sc_parallelextract_covarsfull_productfull_sc_lhoodgenerate_mppinteraction_stpart_1_1_fullpart_1_2_fullpart_1_3_fullpart_1_4_fullpart_1_fullpart_2_fullplot_mpppower_law_mappingpredict_marksscale_rasterssim_spatial_scsim_temporal_scsimulate_mppsimulate_scspat_interactiontemporal_sctoroidal_dist_matrix_optimizedtrain_mark_modelvec_distvec_to_mat_dist
Dependencies:abindbundleclasscliclockclustercodetoolscolorspacecpp11crayondata.tabledeldirdiagramdialsDiceDesigndigestdoFuturedoParalleldplyrfansifarverforeachfurrrfuturefuture.applygenericsGETggplot2globalsgluegoftestgowerGPfitgridExtragtablehardhathmsipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalhslifecyclelistenvlubridatemagrittrMASSMatrixmgcvmodelenvmunsellnlmenloptrnnetnumDerivparallellyparsnippillarpkgconfigpolyclipprettyunitsprodlimprogressprogressrpurrrR6rangerRColorBrewerRcppRcppArmadilloRcppEigenrecipesrlangrpartrsamplescalessfdshapesliderspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsSQUAREMstringistringrsurvivaltensorterratibbletidyrtidyselecttimechangetimeDatetunetzdbutf8vctrsviridisLitewarpwithrworkflowsxgboostyardstick
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Check the fit of estimated self-correcting model on the reference point pattern dataset | check_model_fit |
| Estimate parameters of the self-correcting model using log-likelihood optimization | estimate_parameters_sc |
| Estimate parameters of the self-correcting model using log-likelihood maximization in parallel | estimate_parameters_sc_parallel |
| Extract covariate values from a set of rasters | extract_covars |
| Generate a marked process given locations and marks | generate_mpp |
| 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 |
| Train a flexible model for the mark distribution | train_mark_model |
