Package: TemporalModelR 0.3.0

TemporalModelR: Temporally Explicit Species Distribution Modelling

Increases the ease of implementing a temporally-explicit modeling methodology when building ecological niche and species distribution models. Provides functions to assist with three major steps of temporally-explicit models: (i) preprocessing species and environmental data and generating suitable background or pseudoabsence data, (ii) building a niche model and generating temporally-explicit predictions from that model, and (iii) model postprocessing to explore spatiotemporal trends in model predictions. Methodological and theoretical foundations are described in Ingenloff and Peterson (2021) <doi:10.1111/2041-210X.13564>, Franklin (2010, ISBN:9780521700023), Peterson et al. (2011, ISBN:9780691136882), Blonder (2018) <doi:10.1111/ecog.03187>, Senay et al. (2013) <doi:10.1371/journal.pone.0071218>, and Li and Zhang (2024) <doi:10.48550/arXiv.2404.05933>.

Authors:Connor Hughes [aut, cre], Mariana Castaneda-Guzman [aut], Luis E. Escobar [aut]

TemporalModelR_0.3.0.tar.gz
TemporalModelR_0.3.0.zip(r-4.7)TemporalModelR_0.3.0.zip(r-4.6)TemporalModelR_0.3.0.zip(r-4.5)
TemporalModelR_0.3.0.tgz(r-4.6-any)TemporalModelR_0.3.0.tgz(r-4.5-any)
TemporalModelR_0.3.0.tar.gz(r-4.7-any)TemporalModelR_0.3.0.tar.gz(r-4.6-any)
TemporalModelR_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TemporalModelR/json (API)

# Install 'TemporalModelR' in R:
install.packages('TemporalModelR', repos = c('https://cjhughes926.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cjhughes926/temporalmodelr/issues

Pkgdown/docs site:https://cjhughes926.github.io

Datasets:

On CRAN:

Conda:

5.66 score 15 exports 18 dependencies

Last updated from:9e4baa2567. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK668
source / vignettesOK371
linux-release-x86_64OK309
macos-release-arm64OK260
macos-oldrel-arm64OK247
windows-develOK316
windows-releaseOK266
windows-oldrelOK264
wasm-releaseOK213

Exports:analyze_temporal_patternsanalyze_trends_by_spatial_unitbuild_temporal_gambuild_temporal_glmbuild_temporal_hvbuild_temporal_rfgenerate_absencesgenerate_spatiotemporal_predictionsplot_model_assessmentraster_alignscale_rastersspatiotemporal_partitionspatiotemporal_rarefactionsummarize_raster_outputstemporally_explicit_extraction

Dependencies:classclassIntDBIdeldire1071exactextractrKernSmoothlatticeMASSproxyrasterRcpps2sfspterraunitswk

Preprocessing temporally explicit data
Summary | Overview | Aligning rasters | Spatiotemporal rarefaction | Temporally explicit extraction | Scaling rasters | Spatiotemporal partitioning | Generating pseudoabsences | Buffer method | User data method | Outputs ready for modeling
Rendered fromV2_Preprocessing.Rmdusingknitr::rmarkdown

Last update: 2026-06-29
Started: 2026-05-21

About the Example Dataset
Summary | Description | Overview | Landscape rasters | Elevation | Forest cover and annual precipitation across years | Seasonal precipitation within a year | Occurrence data | Pre-computed objects and other bundled files | Pre-computed data() objects | Intermediate raster and point files
Rendered fromV1_dataset.Rmdusingknitr::rmarkdown

Last update: 2026-06-22
Started: 2026-05-21

3b. Modeling with a GAM
Summary | Theory | Overview | Fitting a temporal GAM | Smooth term syntax | Threshold selection | E-space performance | Projecting predictions | G-space performance | Next steps
Rendered fromV3b_GAM.Rmdusingknitr::rmarkdown

Last update: 2026-06-09
Started: 2026-05-22

3a. Modeling with a GLM
Summary | Theory | Overview | Fitting a temporal GLM | Formula syntax | Threshold selection | E-space performance | Projecting predictions | G-space performance | Next steps
Rendered fromV3a_GLM.Rmdusingknitr::rmarkdown

Last update: 2026-06-09
Started: 2026-05-21

3d. Modeling with a Hypervolume
Summary | Theory | Overview | Fitting a temporal hypervolume | Choosing a method | Tuning hypervolume parameters | E-space performance | Projecting predictions | G-space performance | Next steps
Rendered fromV3d_HV.Rmdusingknitr::rmarkdown

Last update: 2026-06-09
Started: 2026-05-22

3c. Modeling with a Random Forest
Summary | Theory | Overview | Fitting a temporal random forest | Threshold selection | Variable importance | E-space performance | Projecting predictions | G-space performance | Next steps
Rendered fromV3c_RF.Rmdusingknitr::rmarkdown

Last update: 2026-06-09
Started: 2026-05-22

Post-processing predictions
Summary | Overview | Consensus and frequency summarization | Detecting temporal patterns | Aggregating by spatial unit | Summary
Rendered fromV4_Postprocessing.Rmdusingknitr::rmarkdown

Last update: 2026-05-29
Started: 2026-05-21

Readme and manuals

Help Manual

Help pageTopics
Analyze Temporal Patterns in Binary Raster Time Seriesanalyze_temporal_patterns
Summarize Temporal Patterns and Trends by Spatial Unitanalyze_trends_by_spatial_unit
Build Temporal GAM Models Across Cross-Validation Foldsbuild_temporal_gam
Build Temporal GLM Models Across Cross-Validation Foldsbuild_temporal_glm
Build Temporal Hypervolume Models Across Cross-Validation Foldsbuild_temporal_hv
Build Temporal Random Forest Models Across Cross-Validation Foldsbuild_temporal_rf
Bundled rasters, point files, and prediction outputsextdata
Generate Temporally Explicit Pseudoabsence Pointsgenerate_absences
Generate Spatiotemporal Predictions from Temporal Modelsgenerate_spatiotemporal_predictions
Visualize Model Assessment Metrics Across Timeplot_model_assessment
Align and Standardize Raster Files to a Reference Rasterraster_align
Scale Environmental Rasters Using Species Occurrence Datascale_rasters
Spatiotemporal Cross-Validation Partitioningspatiotemporal_partition
Spatiotemporal Rarefaction of Species Occurrence Dataspatiotemporal_rarefaction
Summarize Prediction Rasters into Consensus Outputssummarize_raster_outputs
Extract Time-Aligned Environmental Values at Species Occurrencestemporally_explicit_extraction
Pre-built pseudoabsence result (seasonal workflow)tmr_absences
Pre-built pseudoabsence result (annual workflow)tmr_absences_annual
Pre-built user_data pseudoabsences (seasonal workflow)tmr_absences_user
Pre-built GLM result (seasonal workflow)tmr_glm
Pre-built GLM result (annual workflow)tmr_glm_annual
Pre-built spatiotemporal partition (seasonal workflow)tmr_partition
Pre-built spatiotemporal partition (annual workflow)tmr_partition_annual
Pre-built spatiotemporal partition, small version (seasonal workflow)tmr_partition_small
Pre-built spatiotemporal predictions (seasonal workflow)tmr_predictions
Pre-built spatiotemporal predictions (annual workflow)tmr_predictions_annual