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arxiv: 2607.01976 · v1 · pith:MY6JDJZUnew · submitted 2026-07-02 · 📊 stat.ME

Plausibility: Exact inference in R

Stefan B\"ohringer , Jesse Swen This is my paper

Pith reviewed 2026-07-03 08:01 UTC · model grok-4.3

classification 📊 stat.ME
keywords exact inferenceparametric familiesregression modelspenalized regressionR packagetheoretical frameworkclass extension
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The pith

Plausibility is a framework for exact inference in general parametric families, implemented as an R package for regression models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Plausibility as a theoretical framework enabling exact inference in general parametric families. An R package implements this for a wide class of regression models, including penalized ones. The package supports testing such models and is designed to be extensible through a class-based mechanism. Illustrations use various R data sets, and computational aspects are discussed for their effect on real-data analysis.

Core claim

Plausibility is a theoretical framework that allows to conduct exact inference in general parametric families. The R package plausibility implements this framework for a wide class of regression models and can be used to test penalized regression models.

What carries the argument

The Plausibility framework, which provides exact inference for parametric families through its implementation in regression models.

If this is right

  • Exact inference becomes feasible for regression models where only approximate methods were available before.
  • Penalized regression models can be tested exactly within the same framework.
  • The class-based design permits extension to new model families without altering the core implementation.
  • Computational trade-offs determine which analyses can be completed in practice with exact methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could support exact testing in user-defined parametric families outside standard regression.
  • Wider use might shift practice away from asymptotic approximations toward exact results in moderate-sized data.
  • Integration patterns from the R implementation suggest similar packages could be built for other statistical environments.

Load-bearing premise

The theoretical framework can be correctly implemented in software to deliver exact inference for a wide class of regression models including penalized ones.

What would settle it

A simulation or real-data check where the package outputs p-values or intervals that deviate from independently computed exact values under the null.

Figures

Figures reproduced from arXiv: 2607.01976 by Jesse Swen, Stefan B\"ohringer.

Figure 1
Figure 1. Figure 1: Plausibility region for the regression model of the [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Plausibility region for the regression model of the [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Penalized regressions. X-axis are predictors 1, ..., 6033 with regression coefficients [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
read the original abstract

Plausbility is a theoretical framework that allows to conduct exact inference in general parametric families. We introduce R-packages {\em plausibility} that implements this framework for a wide class of regression models. Plausibility can also be used to test penalized regression models such as estimated by package {\em glmnet}. We illustrate the package using a number of R data sets Through a class-based mechanism, the package can be easily extended. We illustrate and discuss computation aspects of the implementation and their impact on real-data analysis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents 'Plausibility' as a theoretical framework for exact inference in general parametric families. It describes an R package of the same name that implements the framework for a wide class of regression models. The package is claimed to enable exact inference for penalized regression models such as those produced by the glmnet package. Illustrations are provided using several R datasets, the package is designed to be extensible via a class-based mechanism, and computational aspects of the implementation are discussed along with their implications for real-data analysis.

Significance. Should the plausibility framework deliver exact, non-asymptotic inference that properly accounts for penalization effects in regularized regression, the work would be significant for the field of statistical methodology. It would offer a practical tool for obtaining exact p-values and confidence intervals in settings where asymptotic approximations or bootstrap methods are currently the norm, particularly for high-dimensional data analysis.

major comments (2)
  1. [Abstract] The assertion that plausibility allows exact inference for glmnet output lacks any derivation or description of the exact pivot or how the regularization is incorporated to maintain exact type I error control. This is load-bearing for the central claim, as penalized estimators generally produce biased estimates and non-pivotal distributions.
  2. [Computation aspects section] No explicit verification or proof is given that the implementation achieves exact (rather than approximate) inference for the highlighted class of penalized models.
minor comments (1)
  1. [Abstract] Typo or formatting issue in 'We illustrate the package using a number of R data sets Through a class-based mechanism,' which appears to be missing punctuation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed report and the opportunity to respond. The comments correctly identify that the current manuscript does not supply a derivation or verification for the exact-inference claim on penalized models. We address each point below and will revise the manuscript to include the requested material.

read point-by-point responses

  1. Referee: [Abstract] The assertion that plausibility allows exact inference for glmnet output lacks any derivation or description of the exact pivot or how the regularization is incorporated to maintain exact type I error control. This is load-bearing for the central claim, as penalized estimators generally produce biased estimates and non-pivotal distributions.

    Authors: We agree that the abstract statement is not supported by a derivation in the present text. The plausibility framework is defined for general parametric families; the claim for glmnet models rests on treating the fixed penalty as part of the model specification, but no explicit pivot or type-I-error argument is given. We will add a dedicated subsection deriving the pivot and showing exact control under the penalized likelihood. revision: yes

  2. Referee: [Computation aspects section] No explicit verification or proof is given that the implementation achieves exact (rather than approximate) inference for the highlighted class of penalized models.

    Authors: The computation section discusses algorithmic complexity and numerical stability but contains no formal verification that the implemented procedure yields exact (non-asymptotic) inference for the penalized case. We will insert a short proof sketch or reference to the theoretical guarantee together with a small simulation check confirming exact type-I-error control. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework introduced as independent theoretical contribution

full rationale

The paper defines Plausibility as a new theoretical framework for exact inference in general parametric families and describes its R implementation for regression models including penalized ones. No derivation chain, equations, or self-citations are exhibited in the provided text that reduce a claimed prediction or result to fitted inputs or prior self-work by construction. The central claim is the existence and applicability of the framework itself, which stands as a self-contained proposal rather than a reduction of outputs to inputs. Standard software-paper risks around implementation fidelity are noted by the skeptic but do not constitute circularity under the specified rules, as no load-bearing step is quoted that collapses by definition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No information on free parameters, axioms, or invented entities available from the abstract.

pith-pipeline@v0.9.1-grok · 5600 in / 944 out tokens · 35967 ms · 2026-07-03T08:01:21.991150+00:00 · methodology

discussion (0)

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