Changes in version 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

                        Changes in version 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