Package: ldmppr 1.0.4.9000

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:Lane Drew [aut, cre, cph], Andee Kaplan [aut]

ldmppr_1.0.4.9000.tar.gz
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ldmppr_1.0.4.9000.tar.gz(r-4.5-noble)ldmppr_1.0.4.9000.tar.gz(r-4.4-noble)
ldmppr_1.0.4.9000.tgz(r-4.4-emscripten)ldmppr_1.0.4.9000.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

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

5.00 score 1 stars 2 scripts 329 downloads 33 exports 112 dependencies

Last updated 1 months agofrom:ffbddc1e89. Checks:12 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 26 2025
R-4.5-win-x86_64OKMar 26 2025
R-4.5-mac-x86_64OKMar 26 2025
R-4.5-mac-aarch64OKMar 26 2025
R-4.5-linux-x86_64OKMar 26 2025
R-4.4-win-x86_64OKMar 26 2025
R-4.4-mac-x86_64OKMar 26 2025
R-4.4-mac-aarch64OKMar 26 2025
R-4.4-linux-x86_64OKMar 26 2025
R-4.3-win-x86_64OKMar 26 2025
R-4.3-mac-x86_64OKMar 26 2025
R-4.3-mac-aarch64OKMar 26 2025

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.applygenericsGETggplot2globalsgluegoftestgowerGPfitgridExtragtablehardhathmsipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalhslifecyclelistenvlubridatemagrittrMASSMatrixmgcvmodelenvmunsellnlmenloptrnnetnumDerivparallellyparsnippillarpkgconfigpolyclipprettyunitsprodlimprogressprogressrpurrrR6rangerRColorBrewerRcppRcppArmadilloRcppEigenrecipesrlangrpartrsamplescalessfdshapeslidersparsevctrsspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsSQUAREMstringistringrsurvivaltensorterratibbletidyrtidyselecttimechangetimeDatetunetzdbutf8vctrsviridisLitewarpwithrworkflowsxgboostyardstick

Example ldmppr Workflow on Simulated Data

Rendered fromldmppr_howto.Rmdusingknitr::rmarkdownon Mar 26 2025.

Last update: 2025-01-29
Started: 2024-11-23