R: Accuracy Statistic Estimation for Imperfect Gold Standards

# Accuracy Statistic Estimation for Imperfect Gold Standards

## Documentation for package ‘emery’ version 0.7.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

aggregate_boot_ML | Aggregate bootstrapped ML estimates
bin_auc | Calculate AUC for single Se/Sp pair
boot_ML | Bootstrap ML accuracy statistic estimation for multi-method data
censor_data | Censor data randomly rowwise
define_disease_state | Define the True disease state of a simulated sample
dmvnorm | Multivariate Normal Densities
estimate_ML | Estimate maximum likelihood accuracy statistics by expectation maximization
estimate_ML_binary | Estimate maximum likelihood accuracy statistics by expectation maximization
estimate_ML_continuous | Estimate maximum likelihood accuracy statistics by expectation maximization
estimate_ML_ordinal | Estimate maximum likelihood accuracy statistics by expectation maximization
generate_multimethod_binary | Create data sets which simulate paired measurements of multiple methods
generate_multimethod_continuous | Create data sets which simulate paired measurements of multiple methods
generate_multimethod_data | Create data sets which simulate paired measurements of multiple methods
generate_multimethod_ordinal | Create data sets which simulate paired measurements of multiple methods
getNames | Return names from MultiMethodMLEstimate object
getNames-method | Return names from MultiMethodMLEstimate object
getResults | Return result from MultiMethodMLEstimate object
getResults-method | Return result from MultiMethodMLEstimate object
MultiMethodMLEstimate-class | S4 object containing the results of multi-method ML accuracy estimates
name_thing | Create unique names for a set of things
new_boot_ML | Create new boot_ML class object
plot-method | Create plots from a MultiMethodMLEstimate object
plot.boot_ML | Plot univariate distributions of bootstrapped ML estimates
plot_ML | Create plots visualizing the ML estimation process and results.
plot_ML_binary | Create plots visualizing the ML estimation process and results.
plot_ML_continuous | Create plots visualizing the ML estimation process and results.
plot_ML_ordinal | Create plots visualizing the ML estimation process and results.
pollinate_ML | Generate seed values for EM algorithm
pollinate_ML_binary | Generate seed values for EM algorithm
pollinate_ML_continuous | Generate seed values for EM algorithm
pollinate_ML_ordinal | Generate seed values for EM algorithm
random_start | Generate a random initial starting point for EM algorithm
random_start_binary | Generate a random initial starting point for EM algorithm
random_start_ordinal | Generate a random initial starting point for EM algorithm
setFreqs | Set frequencies of MultiMethodMLEstimate objects
setFreqs-method | Set frequencies of MultiMethodMLEstimate objects
show-method | Show a MultiMethodMLEstimate S4 object
unique_obs_summary | Reduce Data by Summarizing Observations