metafrontier 0.2.2

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.

metafrontier 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.

metafrontier 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.