| add likelihood for a BrS measurement slice among cases (conditional dependence) | add_meas_BrS_case_Nest_Slice |
| add likelihood for a BrS measurement slice among cases (conditional dependence) | add_meas_BrS_case_Nest_Slice_jags |
| add likelihood component for a BrS measurement slice among cases | add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags |
| add likelihood component for a BrS measurement slice among cases | add_meas_BrS_case_NoNest_reg_Slice_jags |
| add a likelihood component for a BrS measurement slice among cases (conditional independence) | add_meas_BrS_case_NoNest_Slice |
| add a likelihood component for a BrS measurement slice among cases (conditional independence) | add_meas_BrS_case_NoNest_Slice_jags |
| add likelihood for a BrS measurement slice among controls (conditional independence) | add_meas_BrS_ctrl_Nest_Slice |
| add a likelihood component for a BrS measurement slice among controls | add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags |
| add a likelihood component for a BrS measurement slice among controls | add_meas_BrS_ctrl_NoNest_reg_Slice_jags |
| add a likelihood component for a BrS measurement slice among controls (conditional independence) | add_meas_BrS_ctrl_NoNest_Slice |
| add parameters for a BrS measurement slice among cases and controls | add_meas_BrS_param_Nest_reg_Slice_jags |
| add parameters for a BrS measurement slice among cases and controls (conditional dependence) | add_meas_BrS_param_Nest_Slice |
| add parameters for a BrS measurement slice among cases and controls (conditional dependence) | add_meas_BrS_param_Nest_Slice_jags |
| add parameters for a BrS measurement slice among cases and controls | add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags |
| add parameters for a BrS measurement slice among cases and controls | add_meas_BrS_param_NoNest_reg_Slice_jags |
| add parameters for a BrS measurement slice among cases and controls (conditional independence) | add_meas_BrS_param_NoNest_Slice |
| add parameters for a BrS measurement slice among cases and controls (conditional independence) | add_meas_BrS_param_NoNest_Slice_jags |
| add subclass indicators for a BrS measurement slice among cases and controls (conditional independence) | add_meas_BrS_subclass_Nest_Slice |
| add likelihood for a SS measurement slice among cases (conditional independence) | add_meas_SS_case |
| add parameters for a SS measurement slice among cases (conditional independence) | add_meas_SS_param |
| convert one column data frame to a vector | as.matrix_or_vec |
| Interpret the specified model structure | assign_model |
| baker: *B*ayesian *A*nalytic *K*it for *E*tiology *R*esearch | baker |
| Pick parameters in the Beta distribution to match the specified range | beta_parms_from_quantiles |
| Plot beta density | beta_plot |
| Convert a 0/1 binary-coded sequence into decimal digits | bin2dec |
| check existence and create folder if non-existent | check_dir_create |
| Combine subsites in raw PERCH data set | clean_combine_subsites |
| Clean PERCH data | clean_perch_data |
| combine multiple data_nplcm (useful when simulating data from regression models) | combine_data_nplcm |
| Calculate marginal log odds ratios | compute_logOR_single_cause |
| compute positive rates for nested model with subclass mixing weights that are the same across 'Jcause' classes for each person (people may have different weights.) | compute_marg_PR_nested_reg |
| compute positive rates for nested model with subclass mixing weights that are the same across 'Jcause' classes for each person (people may have different weights.) | compute_marg_PR_nested_reg_array |
| create regressor summation equation used in regression for etiology | create_bugs_regressor_Eti |
| create regressor summation equation used in regression for FPR | create_bugs_regressor_FPR |
| Simulated dataset that is structured in the format necessary for an 'nplcm()' without regression | data_nplcm_noreg |
| Simulated dataset that is structured in the format necessary for an 'nplcm()' with regression | data_nplcm_reg_nest |
| Deletes a pattern from the start of a string, or each of a vector of strings. | delete_start_with |
| Make etiology design matrix for dates with R format. | dm_Rdate_Eti |
| Make FPR design matrix for dates with R format. | dm_Rdate_FPR |
| expit function | expit |
| Import Raw PERCH Data 'extract_data_raw' imports and converts the raw data to analyzable format | extract_data_raw |
| Obtain coverage status from a result folder | get_coverage |
| Obtain direct bias that measure the discrepancy of a posterior distribution of pie and a true pie. | get_direct_bias |
| get fitted mean for nested model with subclass mixing weights that are the same among cases | get_fitted_mean_nested |
| get model fitted mean for conditional independence model | get_fitted_mean_no_nested |
| get individual data | get_individual_data |
| get individual prediction (Bayesian posterior) | get_individual_prediction |
| get index of latent status | get_latent_seq |
| get marginal TPR and FPR for nested model | get_marginal_rates_nested |
| get marginal TPR and FPR for no nested model | get_marginal_rates_no_nested |
| Obtain Integrated Squared Aitchison Distance, Squared Bias and Variance (both on Central Log-Ratio transformed scale) that measure the discrepancy of a posterior distribution of pie and a true pie. | get_metric |
| get etiology samples by names (no regression) | get_pEti_samp |
| get the plotting positions (numeric) for the fitted means; 3 positions for each cell | get_plot_num |
| get a list of measurement index where to look for data | get_plot_pos |
| Obtain posterior standard deviation from a result folder | get_postsd |
| get top patterns from a slice of bronze-standard measurement | get_top_pattern |
| Shannon entropy for multivariate discrete data | H |
| test if a formula has terms not created by [s_date_Eti() or 's_date_FPR()' | has_non_basis |
| Convert 0/1 coding to pathogen/combinations | I2symb |
| Convert a matrix of binary indicators to categorical variables | Imat2cat |
| Initialize individual latent status (for 'JAGS') | init_latent_jags_multipleSS |
| insert distribution for latent status code chunk into .bug file | insert_bugfile_chunk_noreg_etiology |
| Insert measurement likelihood (without regression) code chunks into .bug model file | insert_bugfile_chunk_noreg_meas |
| insert etiology regression for latent status code chunk into .bug file; discrete predictors | insert_bugfile_chunk_reg_discrete_predictor_etiology |
| Insert measurement likelihood (with regression; discrete) code chunks into .bug model file | insert_bugfile_chunk_reg_discrete_predictor_nonest_meas |
| insert etiology regression for latent status code chunk into .bug file | insert_bugfile_chunk_reg_etiology |
| Insert measurement likelihood (nested model+regression) code chunks into .bug model file | insert_bugfile_chunk_reg_nest_meas |
| Insert measurement likelihood (with regression) code chunks into .bug model file | insert_bugfile_chunk_reg_nonest_meas |
| Check if covariates are discrete | is_discrete |
| check if the formula is intercept only | is_intercept_only |
| See if a result folder is obtained by JAGS | is_jags_folder |
| check if a list has elements all of length one | is_length_all_one |
| Test for 'try-error' class | is.error |
| Run 'JAGS' from R | jags2_baker |
| convert line to user coordinates | line2user |
| load an object from .RDATA file | loadOneName |
| logit function | logit |
| calculate pairwise log odds ratios | logOR |
| log sum exp trick | logsumexp |
| Get position to store in data_nplcm$Mobs: | lookup_quality |
| Create new file name | make_filename |
| Create new folder name | make_foldername |
| Takes any number of R objects as arguments and returns a list whose names are derived from the names of the R objects. | make_list |
| Make measurement slice | make_meas_object |
| Make a list with numbered names | make_numbered_list |
| make a mapping template for model fitting | make_template |
| Shannon entropy for binary data | marg_H |
| Match latent causes that might have the same combo but different specifications | match_cause |
| For a list of many sublists each of which has matrices as its member, we combine across the many sublists to produce a final list | merge_lists |
| Reorder the measurement dimensions to match the order for display | my_reorder |
| convert 'NA' to '.' | NA2dot |
| Fit nested partially-latent class models (highest-level wrapper function) | nplcm |
| Fit nested partially-latent class model (low-level) | nplcm_fit_NoReg |
| Fit nested partially-latent class model with regression (low-level) | nplcm_fit_Reg_discrete_predictor_NoNest |
| Fit nested partially-latent class model with regression (low-level) | nplcm_fit_Reg_Nest |
| Fit nested partially-latent class model with regression (low-level) | nplcm_fit_Reg_NoNest |
| Read data and other model information from a folder that stores model results. | nplcm_read_folder |
| Convert 'NULL' to zero. | null_as_zero |
| order latent status by posterior mean | order_post_eti |
| specify overall uniform (symmetric Dirichlet distribution) for etiology prior | overall_uniform |
| parse regression components (either false positive rate or etiology regression) for fitting npLCM; Only use this when formula is not 'NULL'. | parse_nplcm_reg |
| pathogens and their categories in PERCH study (virus or bacteria) | pathogen_category_perch |
| Hypothetical pathogens and their categories (virus or bacteria) | pathogen_category_simulation |
| Plot bronze-standard (BrS) panel | plot_BrS_panel |
| visualize the PERCH etiology regression with a continuous covariate | plot_case_study |
| Posterior predictive checking for the nested partially class models - frequent patterns in the BrS data. (for multiple folders) | plot_check_common_pattern |
| Posterior predictive checking for nested partially latent class models - pairwise log odds ratio (only for bronze-standard data) | plot_check_pairwise_SLORD |
| visualize the etiology regression with a continuous covariate | plot_etiology_regression |
| visualize the etiology estimates for each discrete levels | plot_etiology_strat |
| plotting the labels on the left margin for panels plot | plot_leftmost |
| Visualize pairwise log odds ratios (LOR) for data that are available in both cases and controls | plot_logORmat |
| Plot three-panel figures for nested partially-latent model results | plot_panels |
| Plot etiology (pie) panel | plot_pie_panel |
| Plot silver-standard (SS) panel | plot_SS_panel |
| visualize the subclass weight regression with a continuous covariate | plot_subwt_regression |
| 'plot.nplcm' plot the results from 'nplcm()'. | plot.nplcm |
| 'print.nplcm' summarizes the results from 'nplcm()'. | print.nplcm |
| Compact printing of 'nplcm()' model fits | print.summary.nplcm.no_reg |
| Compact printing of 'nplcm()' model fits | print.summary.nplcm.reg_nest |
| Compact printing of 'nplcm()' model fits | print.summary.nplcm.reg_nest_strat |
| Compact printing of 'nplcm()' model fits | print.summary.nplcm.reg_nonest |
| Compact printing of 'nplcm()' model fits | print.summary.nplcm.reg_nonest_strat |
| Read measurement slices | read_meas_object |
| Sample a vector of Bernoulli variables. | rvbern |
| Make Etiology design matrix for dates with R format. | s_date_Eti |
| Make false positive rate (FPR) design matrix for dates with R format. | s_date_FPR |
| Set true positive rate (TPR) prior ranges for bronze-standard (BrS) data | set_prior_tpr_BrS_NoNest |
| Set true positive rate (TPR) prior ranges for silver-standard data. | set_prior_tpr_SS |
| Stratification setup by covariates | set_strat |
| Show function dependencies | show_dep |
| get an individual's data from the output of 'clean_perch_data()' | show_individual |
| Simulate Bronze-Standard (BrS) Data | simulate_brs |
| Simulate Latent Status: | simulate_latent |
| Simulate data from nested partially-latent class model (npLCM) family | simulate_nplcm |
| Simulate Silver-Standard (SS) Data | simulate_ss |
| softmax | softmax |
| subset data from the output of 'clean_perch_data()' | subset_data_nplcm_by_index |
| summarize bronze-standard data | summarize_BrS |
| silver-standard data summary | summarize_SS |
| 'summary.nplcm' summarizes the results from 'nplcm()'. | summary.nplcm |
| get symmetric difference of months from two vector of R-format dates | sym_diff_month |
| Convert names of pathogen/combinations into 0/1 coding | symb2I |
| generate stick-breaking prior (truncated) from a vector of random probabilities | tsb |
| Convert factor to numeric without losing information on the label | unfactor |
| get unique causes, regardless of the actual order in combo | unique_cause |
| Get unique month from Date | unique_month |
| Visualize matrix for a quantity measured on cases and controls (a single number) | visualize_case_control_matrix |
| visualize trend of pathogen observation rate for NPPCR data (both cases and controls) | visualize_season |
| Write .bug model file for model without regression | write_model_NoReg |
| Write .bug model file for regression model without nested subclasses | write_model_Reg_discrete_predictor_NoNest |
| Write '.bug' model file for regression model WITH nested subclasses | write_model_Reg_Nest |
| Write .bug model file for regression model without nested subclasses | write_model_Reg_NoNest |
| function to write bugs model (copied from R2WinBUGS) | write.model |
