Estimate and Simulate from Location Dependent Marked Point Processes


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Documentation for package ‘ldmppr’ version 1.0.3

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check_model_fit Check the fit of estimated self-correcting model on the reference point pattern dataset
estimate_parameters_sc Estimate parameters of the self-correcting model using log-likelihood optimization
estimate_parameters_sc_parallel Estimate parameters of the self-correcting model using log-likelihood maximization in parallel
extract_covars Extract covariate values from a set of rasters
generate_mpp Generate a marked process given locations and marks
plot_mpp Plot a marked point process
power_law_mapping Gentle decay (power-law) mapping function from sizes to arrival times
predict_marks Predict values from the mark distribution
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
train_mark_model Train a flexible model for the mark distribution