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pgt 0.5.0

Revisions from a full peer review of the package as a software submission.

Inference

• boot_pgt() no longer forces the evaluated DMU into every subsample: each unit is scored against the subsample frontier alone, so frontier units carry genuine resampling variation instead of a degenerate zero-width interval. Replicates with an infeasible programme surface as NA; the new per_dmu$n_ok column counts feasible replicates and a warning fires when they run thin. Interval endpoints truncate to [0, max(1, estimate)], respecting above-1 violator scores.
• The default kappa for model = "byprod" now uses the polluting-input dimension of the T2 sub-programme; boot_pgt() warns when a panel technology is subsampled as a cross-section; group-mean intervals are documented as a descriptive stability band (the aggregate theory of Kneip, Simar and Wilson 2015 is not implemented).
• boot_pgt_sensitivity() reports the implied rate exponent kappa_hat from the Politis-Romano-Wolf log-spread regression, so the borrowed DEA rate is checkable, plus the median over strictly positive widths.
• A Monte Carlo coverage study under a known envelope-model DGP ships as inst/simulations/coverage.R; its results are reported in the productivity vignette.

Productivity index

• pgt_ml() gains technology = "wd": the weak-disposability directional distance in the Kuosmanen form, the technology under which Chung, Fare and Grosskopf (1997) and Oh (2010) define the index, making it the faithful Oh (2010) comparator. The free-disposal semantics of the default "wgd" and "envelope" options are now stated plainly and the claim that they coincide with the weak-disposability index when the cap does not bind has been removed (it was not what the kernel computed).

Documentation and validation

• The validation claims are scoped honestly: published-table replications (wgd, fdmo, byprod) are distinguished from analytic hand-computed checks (mb_cost, wd, envelope) and property checks; the replication vignette shows the farm E divergence from Rodseth (2025) Table 3 side by side and explains why the printed row cannot satisfy the materials-balance-forced ratio; the two documented pigfarms mappings are stated and tested to agree.
• Two new vignettes: models states every estimating programme in full, and multiple-pollutants demonstrates multi-pollutant technologies, DMU-specific coefficients and the staged decomposition.
• compare_models() documents that the headline scores are different quantities (EE for mb_cost) and that only rank-based statistics are strictly comparable; mac_curve() documents which margin the curve prices; foundational and origin literature is cited (Ayres and Kneese 1969; Lauwers 2009; Dakpo, Jeanneaux and Latruffe 2016; Chung, Fare and Grosskopf 1997; Chambers, Chung and Fare 1996; Kuosmanen and Podinovski 2009; O’Donnell, Rao and Battese 2008; Hampf 2014; Fare, Grosskopf, Lovell and Yaisawarng 1993), every vignette carries a reference list, and DOIs are attached throughout.
• inst/CITATION added; a package overview with a scope statement replaces the empty pgt-package help page; the README states the package’s scope and labels the experimental and heuristic modules.

pgt 0.4.1

Fixes from a whole-package review.

Inference

• boot_pgt() now builds proper subsampling intervals (Politis and Romano 1994): the subsample distribution is recentred at the point estimate and rescaled by the relative convergence rate (m/L)^kappa, so intervals extend downward from the estimate, matching the direction of the frontier bias. Previous versions built percentile intervals from the raw subsample distribution, which lies entirely at or above the point estimate. The reported se is rescaled the same way. A new kappa argument controls the rate exponent, defaulting to the DEA rate for the model’s effective dimension.
• boot_pgt() restores the caller’s RNG state on exit instead of permanently reseeding the session.

Model correctness

• Multi-pollutant pgt(model = "wgd") now enforces every pollutant’s materials-balance cap on the peer mix, as documented; earlier versions constrained only the selected pollutant.
• pgt(model = "fdmo") warns when material accounts do not close exactly (u'x - v y != b + a): the model imposes the identity as an equality, so open accounts either make the LP infeasible or shift the closure gap into the scores. mb_check() reports the equality residual in a new closure column when abatement is observed.
• A failed sub-LP in pgt(model = "byprod") or "mb_cost" now sets every score of that DMU to NA, so status != 0 always means the scores are NA, matching the warning text and boot_pgt().
• pgt(model = "mb_cost") warns when the implied minimal emission b_star is negative (possible under DMU-specific coefficients).
• pgt() and pgt_decompose() warn that x_abate is recorded but not yet used by any estimator.
• pgt_tech() rejects an ambiguous v whose length equals both the number of DMUs and the number of pollutants, and rejects duplicated (id, period) pairs, which pgt_ml() silently mismatched before.

Interface

• compare_models() recognises the documented aliases: wgd_rodseth is accepted, and ddf now yields the clear directional-model rejection instead of a misleading unknown-model error. Duplicated entries are ignored with a warning, and the largest-disagreement line no longer reports a model’s self-correlation.
• mac_curve() and shadow_prices() now error clearly for models without output duals (fdmo, byprod, mb_cost, wd); earlier versions returned an empty curve reporting zero exclusions.
• print() on a subset of an mb_check() audit reports counts for the printed rows, not the full table.
• summary() of a pgt_ml fit no longer errors when transitions contain NA; failed distance LPs are counted and warned about.
• The pollutant line is printed for every model (previously omitted for envelope) and by summary() as well.
• pgt(pollutant = <character vector>) gives a clear error instead of an internal condition-length failure.

Housekeeping

• DMU-invariant quantities are computed once per fit instead of once per DMU (and once per LP solve in boot_pgt()).
• Benchmarking dropped from Suggests (never used).
• Example and vignette runtimes reduced.

pgt 0.4.0

Panel productivity measurement and inference.

• pgt_ml(): the global Malmquist-Luenberger productivity index (Oh
  2010. under the pollution-generating technology, decomposed into efficiency change and best-practice change (GML = EC x BPC). The global reference frontier removes the cross-period infeasibility of the adjacent-period index. Marked experimental: the index under a materials-balance technology is not yet settled in the literature.
• boot_pgt() and boot_pgt_sensitivity(): subsampling (m out of n) confidence intervals for per-DMU scores and group means, with a subsample-size sensitivity check. The intervals are heuristic; the documentation states why.
• pgt_tech() gains a period argument for panel data.
• S3 methods for the new classes pgt_ml and pgt_boot.

pgt 0.3.0

Competing axiom systems and a comparison harness.

• pgt(model = "byprod"): the by-production intersection technology of Murty, Russell and Levkoff (2012). Reports output efficiency, emission efficiency and their graph average. Unit tests reproduce the analytic scores of the paper’s Example 1.
• pgt(model = "mb_cost"): the materials-balance cost model of Coelli, Lauwers and Van Huylenbroeck (2007), with the EE = TE x EAE decomposition into technical and environmental-allocative efficiency.
• pgt(model = "wd"): the weak-disposability model (Kuosmanen 2005 correct VRS formulation) as a reference axiom system.
• compare_models(): fits the efficiency-scored models on one technology and reports Spearman rank agreement, worst-quartile overlap and the largest ranking disagreement.
• pgt_tech() gains a polluting argument marking the emission-causing inputs of the by-production model.
• Four package vignettes: the core workflow, the published-result replications, the cross-axiom comparison, and productivity with inference.

pgt 0.2.0

The directional representation and richer material accounting.

• pgt(model = "fdmo"): the factorially determined multi-output (directional) representation of Rodseth (2025), Eq. 13 with abatement fixed at each DMU’s own level (alias "ddf"). Jointly expands the good output and contracts the bad output; returns good-output, bad-output and gross efficiency. Unit tests reproduce the paper’s Table 3.
• DMU-specific material flow coefficients: u may be an L x N matrix and v a length-L vector, so material or output quality can vary across units (Eder 2022, Rodseth 2025).
• Multi-pollutant technologies: b may be an L x P matrix, with u a named list and v a vector or matrix; each pollutant carries its own materials-balance identity, and a pollutant argument selects the one to estimate.
• pgt_tech() gains an a (abatement output) argument, required by the directional model.

pgt 0.1.0

Initial release: the Rodseth (2025) weak-G-disposability slice.

• pgt_tech(): pollution-generating technology constructor with materials-balance metadata (material flow coefficients u, v).
• mb_check(): pre-estimation audit of the materials-balance identity u'x - v y >= b.
• pgt(): per-DMU minimum-bad-output estimation; models "wgd" (Rodseth 2025, Eq. 6 reduced form, alias "wgd_rodseth") and "envelope" (Eq. 6 with inputs free: the convex lower (y, b) envelope); VRS/CRS; pooled or group reference sets.
• pgt_decompose(): metafrontier decompositions of environmental efficiency; "envelope" (Total = WR x TGR, exact identity, no feasibility screen) and "rodseth" (TE x Technology x AE from staged relaxations of the full WGD program).
• shadow_prices() and mac_curve(): constraint duals and marginal abatement cost curves.
• S3 print, summary, plot, as.data.frame methods; synthetic steeldemo panel; testthat suite with hand-computed LP checks and a cross-check against an independent reference implementation of the reduced-form program.
• pigfarms data: the numerical example of Rodseth (2025), Table 1. Unit tests replicate the paper’s Table 2 minimal controlled emissions (16, 16, 16, 20, 16) and recover its abatement column from the mb_check() closure gap.

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.