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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.