ValExp_example_fit      ValExp_example_fit
example_output          example_output
example_val_sum         example_val_sum: Example summaries of validated
                        data
mask_FE_all_visits      Mask a proportion of all visits: A function for
                        simulating a fixed effort validation design.
mask_by_spp             mask_by_spp: simulate a validation design
mcmc_sum                MCMC_sum: A custom function for summarizing
                        MCMC posterior sampling
plot_bias_vs_calls      plot_bias_vs_calls: Compare validation designs
                        based on estimation error and expected level of
                        effort
plot_coverage_vs_calls
                        plot_coverage_vs_calls: Compare validation
                        designs based on coverage of 95% posterior
                        intervals and expected level of effort
plot_width_vs_calls     plot_width_vs_calls: Compare validation designs
                        based on 95% posterior interval width and
                        expected level of effort
run_sims                run_sims: conduct simulations easily
sim_dat                 Simulate data from the count-detection model
                        with counts per site-visit
simulate_validatedData
                        Simulate many datasets under candidate
                        validation designs
summarize_n_validated   Summarize the number of validated recordings
tune_mcmc               Get suggested MCMC settings prior to starting
                        your simulations
visualize_parameter_group
                        visualize_parameter_group
visualize_single_parameter
                        visualize_single_parameter
