Package: COMBO 1.2.0

COMBO: Correcting Misclassified Binary Outcomes in Association Studies

Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.

Authors:Kimberly Hochstedler Webb [aut, cre]

COMBO_1.2.0.tar.gz
COMBO_1.2.0.zip(r-4.7)COMBO_1.2.0.zip(r-4.6)COMBO_1.2.0.zip(r-4.5)
COMBO_1.2.0.tgz(r-4.6-any)COMBO_1.2.0.tgz(r-4.5-any)
COMBO_1.2.0.tar.gz(r-4.7-any)COMBO_1.2.0.tar.gz(r-4.6-any)
COMBO_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
COMBO/json (API)

# Install 'COMBO' in R:
install.packages('COMBO', repos = c('https://kimberlywebb.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kimberlywebb/combo/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3
Datasets:
  • COMBO_EM_data - Test data for the COMBO_EM function
  • LSAC_data - Example data from The Law School Admissions Council's
  • VPRAI_synthetic_data - Synthetic example data of pretrial failure risk factors and outcomes, VPRAI recommendations, and judge decisions

On CRAN:

Conda:

jagscpp

4.00 score 1 stars 4 scripts 602 downloads 9 exports 45 dependencies

Last updated from:9385fedf39. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK158
source / vignettesOK222
linux-release-x86_64OK176
macos-release-arm64OK232
macos-oldrel-arm64OK214
windows-develOK131
windows-releaseOK124
windows-oldrelOK135
wasm-releaseOK123

Exports:COMBO_dataCOMBO_data_2stageCOMBO_EMCOMBO_EM_2stageCOMBO_MCMCCOMBO_MCMC_2stagemisclassification_probmisclassification_prob2true_classification_prob

Dependencies:clicodacodetoolscpp11DBIdoParalleldplyrforeachgenericsglueiteratorslatticelifecyclemagrittrMASSMatrixMatrixModelsminqamitoolsnloptrnumDerivoptimxpillarpkgconfigpracmapurrrquantregR6RcppRcppArmadillorjagsrlangSAMBASparseMstringistringrsurveysurvivaltibbletidyrtidyselectturboEMutf8vctrswithr

COMBO Notation Guide

Kim Hochstedler

Rendered fromCOMBO_notation_guide.Rmdusingknitr::rmarkdownon May 15 2026.

Last update: 2024-07-02
Started: 2022-11-01

COMBO Notation Guide - Two-stage Misclassification Model

Kim Hochstedler

Rendered fromCOMBO_notation_guide_2stage.Rmdusingknitr::rmarkdownon May 15 2026.

Last update: 2024-07-02
Started: 2022-12-13

Readme and manuals

Help Manual

Help pageTopics
Check Assumption and Fix Label Switching if Assumption is Broken for a List of MCMC Samplescheck_and_fix_chains
Check Assumption and Fix Label Switching if Assumption is Broken for a List of MCMC Samplescheck_and_fix_chains_2stage
Generate Data to use in COMBO FunctionsCOMBO_data
Generate data to use in two-stage COMBO FunctionsCOMBO_data_2stage
EM-Algorithm Estimation of the Binary Outcome Misclassification ModelCOMBO_EM
EM-Algorithm Estimation of the Two-Stage Binary Outcome Misclassification ModelCOMBO_EM_2stage
Test data for the COMBO_EM functionCOMBO_EM_data
MCMC Estimation of the Binary Outcome Misclassification ModelCOMBO_MCMC
MCMC Estimation of the Two-Stage Binary Outcome Misclassification ModelCOMBO_MCMC_2stage
EM-Algorithm Function for Estimation of the Misclassification Modelem_function
EM-Algorithm Function for Estimation of the Two-Stage Misclassification Modelem_function_2stage
Expit functionexpit
Set up a Binary Outcome Misclassification 'jags.model' Object for a Given Priorjags_picker
Set up a Two-Stage Binary Outcome Misclassification 'jags.model' Object for a Given Priorjags_picker_2stage
Fix Label Switching in MCMC Results from a Binary Outcome Misclassification Modellabel_switch
Fix Label Switching in MCMC Results from a Binary Outcome Misclassification Modellabel_switch_2stage
Expected Complete Data Log-Likelihood Function for Estimation of the Misclassification Modelloglik
Expected Complete Data Log-Likelihood Function for Estimation of the Two-Stage Misclassification Modelloglik_2stage
Example data from The Law School Admissions Council's (LSAC) National Bar Passage Study (Linda Wightman, 1998)LSAC_data
Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjectsmean_pistarjj_compute
Compute Conditional Probability of Each Observed Outcome Given Each True Outcome, for Every Subjectmisclassification_prob
Compute Conditional Probability of Each Second-Stage Observed Outcome Given Each True Outcome and First-Stage Observed Outcome, for Every Subjectmisclassification_prob2
Select a Binary Outcome Misclassification Model for a Given Priormodel_picker
Select a Two-Stage Binary Outcome Misclassification Model for a Given Priormodel_picker_2stage
Set up a Naive Logistic Regression 'jags.model' Object for a Given Priornaive_jags_picker
Set up a Naive Two-Stage Regression 'jags.model' Object for a Given Priornaive_jags_picker_2stage
Observed Data Log-Likelihood Function for Estimation of the Naive Two-Stage Misclassification Modelnaive_loglik_2stage
Select a Logisitic Regression Model for a Given Priornaive_model_picker
Select a Naive Two-Stage Regression Model for a Given Priornaive_model_picker_2stage
EM-Algorithm Estimation of the Binary Outcome Misclassification Model while Assuming Perfect Sensitivityperfect_sensitivity_EM
Compute Probability of Each True Outcome, for Every Subjectpi_compute
Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects for each MCMC Chainpistar_by_chain
Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects for each MCMC Chain for a 2-stage modelpistar_by_chain_2stage
Compute Conditional Probability of Each Observed Outcome Given Each True Outcome, for Every Subjectpistar_compute
Compute Conditional Probability of Each Observed Outcome Given Each True Outcome for a given MCMC Chain, for Every Subjectpistar_compute_for_chains
Compute Conditional Probability of Each Observed Outcome Given Each True Outcome for a given MCMC Chain, for Every Subject for 2-stage modelspistar_compute_for_chains_2stage
Compute the Mean Conditional Probability of Second-Stage Correct Classification, by First-Stage and True Outcome Across all Subjects for each MCMC Chainpitilde_by_chain
Compute Conditional Probability of Each Second-Stage Observed Outcome Given Each True Outcome and First-Stage Observed Outcome, for Every Subjectpitilde_compute
Compute Conditional Probability of Each Observed Outcome Given Each True Outcome for a given MCMC Chain, for Every Subjectpitilde_compute_for_chains
M-Step Expected Log-Likelihood with respect to Betaq_beta_f
M-Step Expected Log-Likelihood with respect to Deltaq_delta_f
M-Step Expected Log-Likelihood with respect to Gammaq_gamma_f
Sum Every "n"th Elementsum_every_n
Sum Every "n"th Element, then add 1sum_every_n1
Compute Probability of Each True Outcome, for Every Subjecttrue_classification_prob
Synthetic example data of pretrial failure risk factors and outcomes, VPRAI recommendations, and judge decisionsVPRAI_synthetic_data
Compute E-step for Binary Outcome Misclassification Model Estimated With the EM-Algorithmw_j
Compute E-step for Two-Stage Binary Outcome Misclassification Model Estimated With the EM-Algorithmw_j_2stage