pcalg, InvariantCausalPrediction, glmnet, backShift, CAM,
kernlab, mgcv, mboost, bnlearn, testthat, huge, flare

Unified interface for the estimation of causal networks, including
the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate
additive noise model), 'bivariateCAM' (bivariate causal additive model),
'CAM' (causal additive model) (from package 'CAM'; the package is
temporarily unavailable on the CRAN repository; formerly available versions
can be obtained from the archive), 'hiddenICP' (invariant
causal prediction with hidden variables), 'ICP' (invariant causal prediction)
(from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence
search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC
Algorithm), 'FCI' (fast causal inference),
'RFCI' (really fast causal inference) (all from package 'pcalg') and
regression.

Estimate diagnostic accuracy (sensitivity, specificity, etc) and
agreement statistics when one test is conducted on only a
subsample of specimens
A standard test is observed on all specimens.  We treat
the second test (or sampled test) as being conducted on only a
stratified sample of specimens.  We treat the total sample as
stratified two-phase sampling and use inverse probability
weighting.  We estimate diagnostic accuracy (category-specific
classification probabilities; for binary tests reduces to
specificity and sensitivity) and agreement statistics (percent
agreement, percent agreement by category, Kappa (unweighted),
and symmetry tests (reduces to McNemar's test for binary
tests)).

Correct for Verification Bias in Diagnostic Accuracy & Agreement
A standard test is observed on all specimens.  We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens.  Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control.  We treat the total sample as stratified two-phase sampling and use inverse probability weighting.  We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)).  See: Katki HA, Li Y, Edelstein DW, Castle PE.  Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5): 10.1002/sim.4422.