R: Estimate and Simulate from Location Dependent Marked Point Processes

# Estimate and Simulate from Location Dependent Marked Point Processes

## Documentation for package ‘ldmppr’ version 1.1.3

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

## Help Pages

as.data.frame.ldmppr_budgets | Optimization budget specification object
as.data.frame.ldmppr_fit | Fitted point-process model object
as.data.frame.ldmppr_grids | Grid schedule object
as.data.frame.ldmppr_sim | Simulated marked point process object
as.list.ldmppr_budgets | Optimization budget specification object
as.list.ldmppr_grids | Grid schedule object
as_nloptr | Fitted point-process model object
as_nloptr.ldmppr_fit | Fitted point-process model object
check_model_fit | Check the fit of an estimated model using global envelope tests
coef.ldmppr_fit | Fitted point-process model object
estimate_process_parameters | Estimate point process parameters using log-likelihood maximization
extract_covars | Extract covariate values from a set of rasters
generate_mpp | Generate a marked process given locations and marks
ldmppr_budgets | Create an optimization budget specification for estimate_process_parameters()
ldmppr_budgets-class | Optimization budget specification object
ldmppr_fit | Fitted point-process model object
ldmppr_grids | Create a grid schedule for estimate_process_parameters()
ldmppr_grids-class | Grid schedule object
ldmppr_mark_model | Mark model object
ldmppr_model_check | Model fit diagnostic object
ldmppr_sim | Simulated marked point process object
length.ldmppr_budgets | Optimization budget specification object
length.ldmppr_grids | Grid schedule object
load_mark_model | Mark model object
logLik.ldmppr_fit | Fitted point-process model object
medium_example_data | Medium Example Data
mpp.ldmppr_sim | Simulated marked point process object
nobs.ldmppr_fit | Fitted point-process model object
nobs.ldmppr_sim | Simulated marked point process object
plot.ldmppr_fit | Fitted point-process model object
plot.ldmppr_model_check | Model fit diagnostic object
plot.ldmppr_sim | Simulated marked point process object
plot_mpp | Plot a marked point process
power_law_mapping | Gentle decay (power-law) mapping function from sizes to arrival times
predict.ldmppr_mark_model | Mark model object
predict_marks | Predict values from the mark distribution
print.ldmppr_budgets | Optimization budget specification object
print.ldmppr_fit | Fitted point-process model object
print.ldmppr_grids | Grid schedule object
print.ldmppr_mark_model | Mark model object
print.ldmppr_model_check | Model fit diagnostic object
print.ldmppr_sim | Simulated marked point process object
print.summary.ldmppr_budgets | Optimization budget specification object
print.summary.ldmppr_fit | Fitted point-process model object
print.summary.ldmppr_grids | Grid schedule object
print.summary.ldmppr_mark_model | Mark model object
print.summary.ldmppr_model_check | Model fit diagnostic object
print.summary.ldmppr_sim | Simulated marked point process object
save_mark_model | Mark model object
save_mark_model.ldmppr_mark_model | Mark model object
scale_rasters | Scale a set of rasters
simulate_mpp | Simulate a realization of a location dependent marked point process
simulate_sc | Simulate from the self-correcting model
small_example_data | Small Example Data
summary.ldmppr_budgets | Optimization budget specification object
summary.ldmppr_fit | Fitted point-process model object
summary.ldmppr_grids | Grid schedule object
summary.ldmppr_mark_model | Mark model object
summary.ldmppr_model_check | Model fit diagnostic object
summary.ldmppr_sim | Simulated marked point process object
thin_st_fast | calculates acceptance for thinning mechanism during simulation
train_mark_model | Train a flexible model for the mark distribution
[.ldmppr_budgets | Optimization budget specification object
[.ldmppr_grids | Grid schedule object