Changes in version 0.2.0 (2026-06-11)                  

Features

  - pmed() now accepts a medfit::ParallelMediationData object and
    computes the joint P_med for k parallel mediators — the probability
    the outcome is higher with all mediators at their treated levels
    than at their control levels: Phi(delta * sum(a*b) /
    sqrt(2*sum(b^2*Vm) + 2*Vy)), recovering the single-mediator formula
    at k = 1. All four methods are supported (plugin,
    parametric_bootstrap, nonparametric_bootstrap, mbco), with the total
    indirect effect sum(a_j b_j). Gaussian outcome and mediators only.
    See vignette("parallel-mediation").

  - pmed(..., method = "mbco") adds a deterministic Model-Based
    Constrained Optimization interval (Tofighi & Kelley, 2020): a
    likelihood-ratio interval for both P_med and the indirect effect a *
    b, obtained by inverting the constrained-likelihood test rather than
    by resampling. Gaussian outcome and mediator (with covariates) and
    any contrast x_ref != x_value; seed-free and
    grid-resolution-independent. Non-Gaussian models still use the
    bootstrap methods.

Fixes

  - print(PmedResult) interpretation line now shows the mediation
    estimand P(Y(1, M(1)) > Y(1, M(0))) (manuscript Definition 1)
    instead of the stale direct-effect notation P(Y_{X*, M_X} > Y_{X,
    M_X}). The computed value was already correct; only the displayed
    notation was wrong. A plain-language gloss was added beneath it.
    README cached output updated to match.

                 Changes in version 0.1.0 (2026-06-06)                  

First GitHub release (non-CRAN).

Features

  - pmed() computes P_med — a scale-free probabilistic effect size for
    causal mediation — from a formula or a medfit::MediationData object,
    with plugin, parametric-bootstrap, and nonparametric-bootstrap
    methods, alongside the indirect effect (a * b).

Fixes

  - pmed() now computes the mediation estimand P(Y(x, M(x)) > Y(x,
    M(x*))) + 0.5 P(=) (manuscript Definition 1): treatment held fixed,
    mediator varied between its treated and control levels with the tie
    term. Previously it computed a direct-effect contrast that depended
    on the direct effect c' and could land on the wrong side of 0.5.
    Verified against the closed form Phi(ab / sqrt(2 (b^2 sigma_M^2 +
    sigma_Y^2))) and the manuscript memory_exp example (P_med = 0.68).
  - Binary/non-Gaussian outcomes now draw Bernoulli responses through
    the link (previously degenerate, returning 0/1), using the new
    medfit family slot.
  - Parametric bootstrap indexes coefficients by name (was selecting
    intercepts as a/b); nonparametric bootstrap refits on the correct
    family (was always Gaussian).

Ecosystem

  - Builds on medfit (>= 0.3.0) for model extraction and the family/link
    slot. Part of the mediationverse ecosystem.

                     Changes in version 0.0.0.9000                      

Major Changes

  - S7 Architecture: The package has been refactored to use the S7
    object-oriented system for robust class definitions and method
    dispatch.
  - Website Redesign: The documentation website now uses the litera
    theme with a clean, academic design matching the rmediation package
    style.
  - Quarto Integration: The package now uses Quarto (.qmd) for the
    README and vignettes, providing modern publishing capabilities.

New Features

  - $P_{med}$ Calculation: Implemented pmed() function to compute the
    probabilistic effect size $P_{med}$ for mediation analysis.
  - GLM Support: Added support for Generalized Linear Models (e.g.,
    logistic regression) for both mediator and outcome.
  - Bootstrap Inference: Added parametric and nonparametric bootstrap
    methods for confidence intervals.
  - lavaan Integration: Added extract_mediation() support for SEM models
    fitted with the lavaan package, including FIML and robust
    estimators.
  - mediation Integration: Added support for extracting mediation
    structures directly from mediation::mediate() objects.
  - Indirect Effect Reporting: pmed() now reports the Indirect Effect
    (product of coefficients) alongside $P_{med}$, including bootstrap
    confidence intervals.

Documentation

  - Expanded README: Added detailed explanation of $P_{med}$, features,
    and examples.
  - New Vignette: Added "Introduction to probmed" vignette demonstrating
    linear and binary outcome examples.
  - Integration Vignettes: Added dedicated vignettes for lavaan and
    mediation package integrations.
  - Comparison Vignette: Added "Comparing probmed Workflows" to guide
    users on choosing the best integration method.