Title: Interface to Diverse Estimation Methods of Causal Networks
Description: 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'),
'hiddenICP' (invariant causal prediction with hidden variables),
'ICP' (invariant causal prediction) (from package 'InvariantCausalPrediction'),
'GIES' (greedy interventional equivalence search),
(all from package 'pcalg') and regression.
Suggests: pcalg, InvariantCausalPrediction, glmnet, backShift, CAM,

Description: 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'), '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), 'RFCI' (really fast causal inference) (all from package 'pcalg') and

Imports: methods, Matrix, expm, data.table
Algorithm), 'FCI' (fast causal inference),
'RFCI' (really fast causal inference) (all from package 'pcalg') and
kernlab, mgcv, bnlearn, testthat, huge, flare

kernlab, mgcv, mboost, bnlearn, testthat, huge, flare

'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

Title: Estimate diagnostic accuracy (sensitivity, specificity, etc) and
agreement statistics when one test is conducted on only a
Author: Hormuzd A. Katki and David W. Edelstein
Description: 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