Function reference • hiddenmeta

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 using data.table
`sample_rds()`	Draw respondent-driven sample (RDS) sample from single study using data.table
`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 using data.table
`get_study_est_ht()`	Horvitz-Thompson prevalence estimator
`get_study_est_sspse()`	SS-PSE population size estimator by Handcock, Gile and Mar
`get_study_est_ss()`	Sequential Sampling (SS) prevalence estimator by Gile (2011)
`get_study_est_chords()`	Chords population size estimatior by Berchenko, Rosenblatt and Frost
`get_study_est_nsum()`	NSUM estimatior
`get_study_est_multiplier()`	Service/Object multiplier estimator
`get_study_est_recapture()`	Mark-recapture estimator for closed population
`get_study_est_mse()`	Multiple Systems estimator of population size for Sparse Capture Data by Chan et al.
`get_study_est_linktrace()`	Estimator of population size based on Link-Tracing Sample by Vincent and Thompson
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)
`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
`read_single_study_params()`	Get Parameters and Design Features of Specific Study
`read_study_params()`	Read Study Designs from Google Spreadsheet

Reference

Study level functions

Multi-study functions

Meta level estimation

Helper functions