Estimate and Simulate from Location Dependent Marked Point Processes

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Documentation for package ‘ldmppr’ version 1.1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

as.data.frame.ldmppr_sim	Simulated marked point process 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_fit	Fitted point-process model object
ldmppr_mark_model	Mark model object
ldmppr_model_check	Model fit diagnostic object
ldmppr_sim	Simulated marked point process 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_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_fit	Fitted point-process model 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_fit	Fitted point-process model object
print.summary.ldmppr_model_check	Model fit diagnostic 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_fit	Fitted point-process model object
summary.ldmppr_model_check	Model fit diagnostic object
train_mark_model	Train a flexible model for the mark distribution