Study-level functions

These function allow to simulate population and sampling and conduct estimation of a specific study

get_study_population()

Simulate single population with given network structure

sample_rds()

Draw respondent-driven sample (RDS) sample from single study

sample_tls()

Draw time-location (TLS) sample from single study

sample_pps()

Draw probability sample proportional to size (PPS) from single study

get_study_estimands()

Get individual study estimands

get_study_est_sspse()

SS-PSE population size estimator by Handcock, Gile and Mar

get_study_est_chords()

Chords population size estimatior by Berchenko, Rosenblatt and Frost

get_study_est_ht()

Horvitz-Thompson prevalence estimator using

get_study_est_rds_ss()

Sequential Sampling (SS) prevalence estimator by Gile (2011)

get_study_est_nsum()

NSUM estimatior

get_study_est_multiplier()

Service/Object multiplier estimator

get_study_est_recapture()

Capture-recapture estimator for closed population

get_study_est_gnsum()

Generalized NSUM estimatior

Multi-study functions

These function allow to simulate several study population and sampling strategies and conduct estimation on them at the same time

get_multi_populations()

Draw population(s) with given network structure for multiple studies

get_multi_samples()

Draw samples from multiple study populations according to proposed strategies

get_multi_estimands()

Calculate estimands from multiple study populations

get_multi_estimates()

Calculate estimators from multiple study samples

Meta-level estimation

These functions allow to diagnose a set of studies or estimate relative bias of sampling-estimation strategies

get_meta_population()

Simulate meta study population using simulation of several studies

get_meta_sample()

Sample sampling-estimator strategies across studies for meta-analysis

get_meta_estimands()

Get meta-analysis estimands for specific study-level estimand

get_meta_estimates()

Use Stan model to estimate bias and estimands

Helper functions

These functions are useful for simulations of networks

rbeta_mod()

Random generation from Beta distribution

sim_block_network()

Handler for Simulation of Network Using Block Model

sim_ergm_network()

Handler for Simulation of Network Using ERGM

gen_block_matrix()

Generate block matrix of link probabilities

gen_group_sizes()

Generate sizes of groups in population

get_rds_boot()

Prepare RDS data for Bootstrap Re-sampling Following Salganik (2006)

get_rescaled_boot()

Modified Function for Rescaled Bootstrap by Rust and Rao (1996)

read_study_params()

Read Study Designs from Google Spreadsheet

read_single_study_params()

Get Parameters and Design Features of Specific Study

get_single_study_design()

Get Parameters and Design Features of Specific Study

get_required_data()

Generate Data Frame With Required Data by Study

get_rmse_plots()

Create Diagnosis Plot of Log Normalized RMSE Across Studies