Package: mbg 1.2.0

Nathaniel Henry

mbg: Model-Based Geostatistics

Modern model-based geostatistics for point-referenced data. This package provides a simple interface to run spatial machine learning models and geostatistical models that estimate a continuous (raster) surface from point-referenced outcomes and, optionally, a set of raster covariates. The package also includes functions to summarize raster outcomes by (polygon) region while preserving uncertainty.

Authors:Nathaniel Henry [aut, cre], Benjamin Mayala [aut], Roy Burstein [ctb], Nafis Sadat [ctb], Michael Richards [ctb], Michael Collison [ctb], Michael Cork [ctb]

mbg_1.2.0.tar.gz
mbg_1.2.0.zip(r-4.7-any)mbg_1.2.0.zip(r-4.6-any)mbg_1.2.0.zip(r-4.5-any)
mbg_1.2.0.tgz(r-4.6-any)mbg_1.2.0.tgz(r-4.5-any)
mbg_1.2.0.tar.gz(r-4.7-any)mbg_1.2.0.tar.gz(r-4.6-any)
mbg_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mbg/json (API)

# Install 'mbg' in R:
install.packages('mbg', repos = c('https://henryspatialanalysis.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/henryspatialanalysis/mbg/issues

Pkgdown/docs site:https://henryspatialanalysis.github.io

On CRAN:

Conda:

6.31 score 46 stars 11 scripts 393 downloads 21 exports 83 dependencies

Last updated from:1dd8b610be. Checks:8 OK, 1 NOTE. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK264
source / vignettesOK213
linux-releaseOK271
macos-releaseOK155
macos-oldrelOK260
windows-develOK209
windows-releaseOK229
windows-oldrelNOTE225
wasm-releaseOK144

Exports:aggregate_draws_to_polygonsaggregate_raster_to_polygonsbuild_aggregation_tablebuild_id_rasterdissolve_sf_by_attributefit_inla_modelgenerate_cell_draws_and_summarizeload_covariateslog_posterior_densitylogging_get_timer_loglogging_start_timerlogging_stop_timermake_time_stampmake_world_template_rasterMbgModelRunnerprepare_inla_data_stackrmse_raster_to_pointrun_regression_submodelssummarize_drawsvalues_to_rastervif_covariate_select

Dependencies:assertthatcaretclassclassIntcliclockcodetoolscpp11data.tableDBIdiagramdigestdplyre1071farverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmatrixStatsModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrparts2S7scalessfshapesparsevctrsSQUAREMstringistringrsurvivalterratibbletictoctidyrtidyselecttimechangetimeDatetzdbunitsutf8vctrsviridisLitewithrwk

All model runner options
1: MBG model basics | 1.A: Input data | 1.B: ID raster | 2: Model family and link function | 3: Model effects | 3.A: Covariate effects | 3.A.i: Standard covariate effect | 3.A.ii: Stacked ensemble model | 3.B: Gaussian process | 3.C: Administrative-level effect | 3.D: Nugget | 4: Aggregation to polygon boundaries | 5: Logging | 6: Further reading
Rendered fromall-model-options.Rmdusingknitr::rmarkdown

Last update: 2025-03-27
Started: 2025-03-27

Getting started with MBG
Setting up | Building MBG model inputs | Fit the geostatistical model | Plot gridded estimates | Plot aggregated estimates
Rendered frommbg.Rmdusingknitr::rmarkdown

Last update: 2025-03-27
Started: 2024-07-24

Model validation and comparison
Predictive validity metrics | Log predictive density | Watanabe-Aikake information criterion | Root mean squared error | Model cross-validation | Setup | In-sample model comparison | Out-of-sample model comparison | Summary | Further reading
Rendered frommodel-comparison.Rmdusingknitr::rmarkdown

Last update: 2025-03-27
Started: 2025-03-27

Running spatial machine learning models
Setup | Running ML models with mbg and caret | Stacking ML models
Rendered fromspatial-ml-models.Rmdusingknitr::rmarkdown

Last update: 2025-03-27
Started: 2025-03-27