# Methods for Optimizing Scales of Effect

## Documentation for package ‘multiScaleR’ version 0.7.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

multiScaleR-package | multiScaleR
aic_tab | multiScaleR model selection
bic_tab | multiScaleR model selection
count_data | Example data frame
diagnostics | Retrieve diagnostics from multiScaleR objects
diagnostics.multiScaleR | Retrieve diagnostics from multiScaleR objects
estimate_multiscale_ram | Estimate Memory Requirements for Multiscale Optimization
hab | Example raster
kernel_dist | Scale Distance
kernel_prep | Kernel Scale Preparation
kernel_scale.raster | Apply kernel smoothing to raster layers
kernel_var | Define multiScaleR covariate transformations
landscape | Simulated raster
landscape_counts | Example data frame
landscape_var | Define multiScaleR covariate transformations
msr_vars | Define multiScaleR covariate transformations
multiScaleR | multiScaleR
multiScale_optim | Multiscale optimization
plot.multiScaleR | Plot method for multiScaleR objects
plot.sigma_profile | Plot Sigma Profile
plot_kernel | Plot kernel densities
plot_marginal_effects | Plot Marginal Effects from a Fitted Model
print.multiScaleR | Print method for multiScaleR
print.multiScaleR_data | Print method for multiScaleR_data
print.summary_multiScaleR | Print method for summary_multiScaleR
profile_sigma | Profile Model Fit Across Sigma Parameter Space
pts | Spatial sample points
sim_dat | Simulate data for optimizing scales of effect
sim_dat_unmarked | Simulate data for optimizing scales of effect with 'unmarked'
sim_rast | Simulate spatially autocorrelated raster surfaces
summary.multiScaleR | Summarize multiScaleR objects
surface_var | Define multiScaleR covariate transformations
surv_pts | Spatial sample points