NEWS

medrobust 0.1.0.9000

New features (2026-06-12)

bound_ci() — confidence intervals for the partial-identification bounds. The raw estimated bound [L̂, Û] is consistent but is not a confidence set; when the identified set is narrow relative to endpoint sampling uncertainty it under-covers the true effect at small samples (e.g. exposure NDE coverage ~0.09 at n=500). bound_ci() applies the Imbens & Manski (2004) construction, widening the endpoints by standard errors obtained by re-evaluating the effect at the fixed optimal sensitivity parameter on resampled data (no grid search per replicate). This restores approximately nominal coverage at small n (exposure NDE 0.09→0.95, NIE 0.12→0.93; mediator NDE 0.19→0.96, NIE 0.39→0.99 at n=500), for both the exposure and mediator paths. See the Identification Mathematics vignette.

Bug fixes (2026-06-11)

(critical) bound_ne() mediator solve mis-specified. The 3×3 linear system in bound_ne_mediator.R built the P01 (Y=0) equation with the Y=1 parameterization, biasing recovery of the true conditional probabilities and therefore the NDE/NIE bounds (NDE overstated, NIE understated, by ~0.05–0.12 on the OR scale; worst under strong differential error). Replaced with two per-outcome 2×2 systems (manuscript §4.2), each solvable iff Sn_y + Sp_y ≠ 1. The exposure path (bound_ne_exposure.R) was audited and found CORRECT (closed-form 2×2 matrix inverse; verified to ~1e-16) — not affected.
simulate_dm_data() true effects. compute_true_effects() computed natural effects by plugging E[M] (and mean-C) into the nonlinear outcome model rather than averaging the outcome over the mediator and confounder distributions (g-computation). This biased the simulation ground truth (NDE_OR ~1.500 vs the correct ~1.480). Corrected to Monte-Carlo g-computation over the empirical confounder distribution; affects the simulator's @true_effects only (the bounds themselves already targeted the correct estimand).
odds_to_prob() boundary. Perfect classification (sn = 1 or sp = 1) produced infinite odds and a downstream NaN; the helper now maps infinite odds to probability 1, so no-misclassification settings (e.g. sn0 = sp0 = 1) work correctly.
Added regression tests: exact-population recovery at the true Ψ (non-differential and differential), agreement of @true_effects with an independent potential-outcome oracle, and bound-contains-truth on large-n simulated data.
Added the Identification Mathematics vignette documenting the estimand, the mediator two-2×2 identification, the exposure closed form, and the finite-sample convergence evidence.

Bug fixes (exposure NIE, 2026-06-11)

(critical) Exposure (A*) NIE bound was incorrect — fixed. With misclassified_variable = "exposure", the NIE bound did not contain the true NIE even at the population limit with the true Ψ in-region (true 1.199 vs bound [0.980, 0.991]); the NDE bound was fine. Root cause: bound_ne_exposure.R recovers the conditional P(A=a | M,Y,C), but the downstream g-computation (compute_effects_from_joint_probs) consumed those values as the joint P(A,M,Y | C), dropping the observed P(M,Y | C) weight. That made the mediator marginal effectively uniform, collapsing P(M|A=1) and P(M|A=0) toward the same shape and driving NIE toward the null while leaving NDE (which fixes the mediator distribution at M(0)) intact. Fix: multiply the recovered conditional by the observed P(M=m, Y=y | C) (M and Y are not misclassified in the exposure scenario) to form the joint. The exposure solve (class-probability inverse) was already correct; only the NIE assembly was wrong. Point test now recovers NDE 1.480 / NIE 1.199 within 0.01; mediator path unaffected.

Ecosystem Notes

Part of the mediationverse ecosystem for mediation analysis
Optionally integrates with medfit for naive estimates
See Ecosystem Coordination for guidelines

medrobust 0.1.0

Initial Release (2025-Q2)

Major Features

bound_ne(): Main function for computing partial identification bounds for Natural Direct Effects (NDE) and Natural Indirect Effects (NIE)
Support for both exposure misclassification and mediator misclassification
Data-driven falsification via testable implications
check_compatibility(): Test specific misclassification parameters against observed data
sensitivity_plot(): Publication-quality sensitivity analysis visualizations
falsification_summary(): Diagnostic summaries of falsified parameter regions
simulate_dm_data(): Generate synthetic data with differential misclassification
extract_bounds(): Extract bounds at specific parameter values
compare_bounds(): Compare bounds across multiple analyses
Bootstrap confidence intervals for bounds

Documentation

Comprehensive package documentation with roxygen2
Getting started vignette
Example datasets: arsenic_synthetic and simulation_results
S3 methods for clean output: print.medrobust_bounds() and summary.medrobust_bounds()

Testing

Basic unit tests with testthat
Input validation for all main functions

Notes

This is the initial development version
Core algorithms for testable implications and identification formulas require user implementation based on their Claude project "measurement error"
Placeholder implementations are marked with TODO comments