Changes in version 0.2.2 (2026-04-21)                  

Bug fixes

  - boot_tgr(): Fixed orientation/rts not propagating to bootstrap
    replicates (always defaulted to output/CRS).
  - boot_tgr(): Fixed hardcoded group column name; now respects the
    user's original group variable.
  - Latent class .loglik_to_u_hat() now respects the dist argument with
    correct JLMS formulas for half-normal, truncated-normal, and
    exponential distributions.
  - Latent class parameter count now correct for truncated-normal (k+3
    instead of k+2).
  - Fixed two incorrect DOIs: Huang et al. (2014) and O'Donnell et al.
    (2008) now link to the correct papers.

Improvements

  - autoplot methods now use proper conditional S3 registration
    (@exportS3Method ggplot2::autoplot) instead of direct export().
  - .extract_benchmarking() now attempts to retrieve XREF/YREF from
    Farrell objects and .estimate_from_models() gives a clear error when
    DEA models lack the required X/y/beta.
  - technology_gap_ratio() documentation now correctly notes that TGR
    can exceed 1 under the stochastic metafrontier.
  - Pre-built vignettes included in inst/doc for reliable R CMD check.

                        Changes in version 0.2.1                        

New features

Panel SFA and time-varying inefficiency

  - simulate_panel_metafrontier(): Simulate balanced panel data with
    time-varying inefficiency and technical change for Monte Carlo
    studies.
  - Panel SFA estimation with BC92 (Battese & Coelli, 1992) and BC95
    (Battese & Coelli, 1995) specifications, supporting time-varying
    inefficiency via the eta parameter.

Bootstrap inference

  - boot_tgr(): Bootstrap confidence intervals for technology gap
    ratios. Supports parametric (residual resampling) and nonparametric
    (case resampling) approaches, with percentile and BCa interval
    types.
  - Parallel bootstrap via ncores argument using the parallel package.
  - S3 methods: print, confint, and plot for boot_tgr objects.

Latent class metafrontier

  - latent_class_metafrontier(): EM algorithm for estimating latent
    class stochastic frontier models within the metafrontier framework.
  - select_n_classes(): Automatic selection of the optimal number of
    latent classes via BIC.
  - S3 methods: print, summary, coef, and efficiencies for
    lc_metafrontier objects.

ggplot2 visualisation

  - autoplot.metafrontier(): Visualise TGR distributions and efficiency
    decompositions using ggplot2.
  - autoplot.malmquist_meta(): Plot Malmquist index components over
    time.
  - autoplot.boot_tgr(): Visualise bootstrap distributions and
    confidence intervals for TGR.

Model interoperability

  - as_metafrontier_model(): Convert pre-fitted model objects from sfaR
    (sfacross), frontier (sfa), and Benchmarking (Farrell) packages into
    metafrontier-compatible format.

Directional distance functions

  - DEA-based metafrontier now supports directional distance functions
    alongside radial efficiency measurement.

Improvements

  - Murphy-Topel variance correction: Stochastic metafrontier standard
    errors now account for the generated-regressor problem using the
    Murphy & Topel (1985) correction with PSD enforcement.
  - Improved numerical stability: Safe Mills ratio computation
    (.safe_mills()) prevents NaN in extreme tail regions for both panel
    SFA and latent class models.
  - Memory-efficient latent class: EM algorithm uses lm.wfit() for
    weighted regression instead of explicit diagonal weight matrices.
  - DEA batch solver: LP object reuse in dea_batch_fast() for improved
    performance on large datasets.
  - Input validation: boot_tgr() now validates R >= 1 with an
    informative error message.
  - All summary() methods now return invisible S3 objects for
    programmatic access, with print() methods for display.
  - Expanded @examples on all exported S3 methods and autoplot
    functions.
  - Three vignettes added: introduction, Malmquist, and methods.

Documentation

  - Comprehensive vignette: "Introduction to metafrontier" covering
    estimation, inference, bootstrap CIs, panel SFA, latent class, and
    directional distance functions.
  - Vignette: "Metafrontier Malmquist Productivity Index" with worked
    panel data examples and identity verification.
  - Vignette: "Metafrontier Methods: Theory and Computation" with
    mathematical details and Monte Carlo comparisons.
  - Expanded @param documentation for control argument in metafrontier()
    with optim options.

                        Changes in version 0.1.0                        

Initial CRAN release.

Estimation

  - metafrontier(): Main estimation function supporting SFA- and
    DEA-based metafrontiers.
  - Deterministic metafrontier (Battese, Rao, and O'Donnell, 2004) via
    constrained optimisation.
  - Stochastic metafrontier (Huang, Huang, and Liu, 2014) via
    second-stage SFA.
  - DEA-based metafrontier with CRS, VRS, DRS, and IRS technology
    assumptions.
  - Half-normal, truncated-normal, and exponential inefficiency
    distributions.
  - Heteroscedastic SFA via formula interface (y ~ x1 + x2 | z1 + z2).
  - Pre-fitted model support via models= argument (sfaR, frontier).

Productivity

  - malmquist_meta(): Metafrontier Malmquist TFP index with three-way
    decomposition (TEC x TGC x TC*) following O'Donnell, Rao, and
    Battese (2008).

Analysis tools

  - technology_gap_ratio() / tgr_summary(): Extract and summarise TGR by
    group.
  - efficiencies(): Extract group, metafrontier, and TGR efficiency
    scores.
  - poolability_test(): Likelihood ratio test for technology
    heterogeneity.
  - simulate_metafrontier(): Data-generating process for Monte Carlo
    studies.

Methods

  - Full S3 method suite: print, summary, coef, vcov, logLik, fitted,
    residuals, nobs, plot, confint, predict.
  - Four built-in plot types: TGR distributions, efficiency scatter,
    decomposition, and frontier comparison.