Package index • ldmppr

All functions

check_model_fit(): Check the fit of an estimated model using global envelope tests
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
print(<ldmppr_fit>) coef(<ldmppr_fit>) logLik(<ldmppr_fit>) summary(<ldmppr_fit>) print(<summary.ldmppr_fit>) plot(<ldmppr_fit>) as_nloptr(): Fitted point-process model object
ldmppr_mark_model() print(<ldmppr_mark_model>) predict.ldmppr_mark_model() save_mark_model() load_mark_model(): Mark model object
print(<ldmppr_model_check>) summary(<ldmppr_model_check>) print(<summary.ldmppr_model_check>) plot(<ldmppr_model_check>): Model fit diagnostic object
print(<ldmppr_sim>) as.data.frame(<ldmppr_sim>) nobs(<ldmppr_sim>) plot(<ldmppr_sim>) mpp.ldmppr_sim(): Simulated marked point process object
medium_example_data: Medium Example Data
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