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 |
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 |
MultiMethodMLEstimate-class |
S4 object containing the results of multi-method ML accuracy estimates |
name_thing |
Create unique names for a set of things |
plot-method |
Create plots from a MultiMethodMLEstimate object |
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 |
show-method |
Show a MultiMethodMLEstimate S4 object |