Package: multiScaleR 0.7.2

Bill Peterman

multiScaleR: Methods for Optimizing Scales of Effect

A tool for optimizing scales of effect when modeling ecological processes in space. Specifically, the scale parameter of a distance-weighted kernel distribution is identified for all environmental layers included in the model. Includes functions to assist in model selection, model evaluation, efficient transformation of raster surfaces using fast Fourier transformation, and projecting models. For more details see Peterman (2026) <doi:10.1007/s10980-025-02267-x>.

Authors:Bill Peterman [aut, cre]

multiScaleR_0.7.2.tar.gz
multiScaleR_0.7.2.zip(r-4.7-x86_64)multiScaleR_0.7.2.zip(r-4.7-arm64)multiScaleR_0.7.2.zip(r-4.6-x86_64)multiScaleR_0.7.2.zip(r-4.6-arm64)
multiScaleR_0.7.2.tar.gz(r-4.7-arm64)multiScaleR_0.7.2.tar.gz(r-4.7-x86_64)multiScaleR_0.7.2.tar.gz(r-4.6-arm64)multiScaleR_0.7.2.tar.gz(r-4.6-x86_64)
multiScaleR_0.7.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
multiScaleR/json (API)

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

Bug tracker:https://github.com/wpeterman/multiscaler/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

cppopenmp

4.16 score 12 scripts 487 downloads 19 exports 64 dependencies

Last updated from:c79432a304. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK282
linux-devel-x86_64OK386
source / vignettesOK320
linux-release-arm64OK274
linux-release-x86_64OK310
windows-devel-arm64OK452
windows-devel-x86_64OK341
windows-release-arm64OK409
windows-release-x86_64OK385
wasm-releaseOK165

Exports:aic_tabbic_tabdiagnosticsestimate_multiscale_ramkernel_distkernel_prepkernel_prep_by_groupkernel_scale.rasterkernel_varlandscape_varmsr_varsmultiScale_optimplot_kernelplot_marginal_effectsprofile_sigmasim_datsim_dat_unmarkedsim_rastsurface_var

Dependencies:AICcmodavgclassclassIntclicowplotcpp11crayonDBIdotCall64dplyre1071exactextractrfarverfieldsgenericsggplot2gluegtableinsightisobandKernSmoothlabelinglatticelifecyclemagrittrmapsMASSMatrixnlmeoptimParallelpillarpkgconfigproxypsclR6rasterrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangs2S7scalessfspspamsurvivalterratibbletidyselectTMBunitsunmarkedutf8vctrsVGAMviridisLitewithrwkxtable

Landscape Metric Covariates
Why explicit covariate specifications? | Landscape metric reference | Composition metrics | Edge metrics | Adjacency metrics | Information theory metrics | Class-level metrics | Load example data | Define derived covariates | Prepare model data | Optimize the kernel covariate | Project derived covariates to rasters | Producing landscape metric rasters without a fitted model | Separating composition from configuration | Optimizing landscape metric radii | Practical guidance | See also | References
Rendered fromlandscape_metric_covariates.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-07-01

multiScaleR Quick-Start Guide
What is multiScaleR? | Installation | The Core Workflow | A Complete Example | Load the package and data | Prepare inputs with kernel_prep() | Fit an initial model | Optimize scales of effect | Examine results | Visualize kernel decay and covariate effects | Diagnose the likelihood surface | Project the fitted model to the landscape | Quick-Reference: Full Workflow | Learn More
Rendered fromquickstart.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-04-13

multiScaleR User Guide
Background | The scale-of-effect problem | What multiScaleR does | A Conceptual Road Map | Distance-weighted Effects | Choosing a kernel | Preparing Data | Explore Data | kernel_prep | Performance and memory for large datasets | Analysis | multiScale_optim | Kernels | Model Selection | Why standard AIC is not enough | Optimization with unmarked | Poisson Count Model | Project model | Binomial Occurrence Model | Other model classes | Other Functions & Features | kernel_dist | plot_kernel | sim_rast | sim_dat
Rendered frommultiScaleR_Guide.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2025-09-02

Surface Texture Covariates
What surface texture covariates measure | Surface metric reference | A note on scale | Load example data | Define derived covariates | Prepare model data and fit | Optimize and interpret | Project covariates to rasters | Roughness surfaces without a fitted model | Distribution-shape and geometry surfaces | Optimizing the scale of roughness | Weighted roughness: the scale of effect of heterogeneity | Practical guidance | Quick reference | Summary of key functions | See also
Rendered fromsurface_metric_covariates.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-07-01

Using Year-Specific Rasters in One Model
Build aligned annual maps and observations | Prepare one pooled set of covariates | Fit one relationship across years | Project the fitted relationship onto each annual map | Quick reference
Rendered fromyear-specific-rasters.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-10-05

Spatial Projection and Clamping
Introduction to Spatial Projection | From fitted model to landscape map | The extrapolation problem | What clamping does | This vignette | Demonstrating Clamping Under Controlled Extrapolation | Conceptual setup | Step 1: Simulate a known landscape and response surface | Step 2: Impose a biased sampling design | Step 3: Fit the multiscale model | The Extrapolation Problem | Seeing extrapolation in practice | Understanding and Applying Clamping | How clamping works | The pct_mx argument: tuning the guardrail | Comparing different clamping settings | Quantifying prediction error: inside vs. outside the sampled domain | Key Takeaways | Practical guidance
Rendered fromspatial_projection_clamping.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-03-26