ensr: R Package for Simultaneous Selection of Elastic Net Tuning Parameters

Peter E. DeWitt; Tellen D. Bennett

arxiv: 1907.00914 · v1 · pith:ZZLDVYIGnew · submitted 2019-07-01 · 📊 stat.CO

ensr: R Package for Simultaneous Selection of Elastic Net Tuning Parameters

Peter E. DeWitt , Tellen D. Bennett This is my paper

Pith reviewed 2026-05-25 11:10 UTC · model grok-4.3

classification 📊 stat.CO

keywords elastic nettuning parametersR packagepenalized regressionlambdaalphamodel selectionglmnet

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The pith

The ensr R package extends glmnet to search the full lambda-alpha space and select an optimal elastic net model.

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

Elastic net regression requires choosing two parameters: lambda for overall penalty strength and alpha for the balance between lasso and ridge penalties. Standard tools allow searching lambda for a fixed alpha but leave users to guess the right alpha. This paper presents the ensr package, which automates a joint search over both parameters to identify the best pair for a given dataset. A sympathetic reader would care because better-tuned models can improve prediction accuracy and variable selection in high-dimensional data with correlated predictors. The package makes this two-dimensional search practical within the R ecosystem.

Core claim

We built the R package ensr that extends the functionality of glmnet to search the λ - α space and identify an optimal λ - α pair.

What carries the argument

The joint search procedure over the two-dimensional λ-α parameter space implemented in the ensr package.

If this is right

Users can obtain elastic net models tuned for both parameters without fixing alpha in advance.
The package enables better control over the compromise between LASSO and ridge in the presence of correlated predictors.
It provides a practical tool for identifying the optimal penalty pair within the R environment.
This extends the capabilities of glmnet for more comprehensive model selection.

Where Pith is reading between the lines

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

The approach could generalize to other penalized regression methods that involve multiple tuning parameters.
It may lead to improved predictive performance in datasets where alpha choice was previously ad hoc.
Developers of statistical software could add similar joint searches to automate tuning in additional model families.

Load-bearing premise

That the search procedure implemented in ensr can identify a meaningfully optimal lambda-alpha pair.

What would settle it

A comparison on held-out data showing that models chosen by ensr produce higher prediction error than glmnet models with manually fixed alphas.

read the original abstract

Motivation: Elastic net regression is a form of penalized regression that lies between ridge and least absolute shrinkage and selection operator (LASSO) regression. The elastic net penalty is a powerful tool controlling the impact of correlated predictors and the overall complexity of generalized linear regression models. The elastic net penalty has two tuning parameters: ${\lambda}$ for the complexity and ${\alpha}$ for the compromise between LASSO and ridge. The R package glmnet provides efficient tools for fitting elastic net models and selecting ${\lambda}$ for a given ${\alpha}.$ However, glmnet does not simultaneously search the ${\lambda} - {\alpha}$ space for the optional elastic net model. Results: We built the R package ensr, elastic net searcher. enser extends the functionality of glment to search the ${\lambda} - {\alpha}$ space and identify an optimal ${\lambda} - {\alpha}$ pair. Availability: ensr is available from the Comprehensive R Archive Network at https://cran.r-project.org/package=ensr

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.

Desk Editor's Note private letter to a colleague

ensr is a basic R wrapper for joint lambda-alpha search in elastic net, but the paper supplies no algorithm, objective, or validation details.

read the letter

The main takeaway is that this is an announcement for the ensr R package, which adds simultaneous search over both lambda and alpha to glmnet's elastic net fitting. glmnet already picks lambda for a fixed alpha; ensr extends that to find a pair. That fills a small practical need for users who want to tune the mixing parameter without manual loops. The package is on CRAN, so anyone can try it directly. That is the extent of what is new and what works: a convenience tool for a routine task. No new statistical method or theoretical result appears. The write-up is short and stays within the bounds of a software note. The central weakness is the complete absence of any description of the search procedure. Nothing is said about whether it uses a grid, an optimizer, what loss or CV metric declares optimality, the number of folds, or how ties are broken. Without those pieces the claim that it identifies an optimal pair cannot be checked or reproduced. There are also no benchmarks, timing results, or comparisons against separate alpha searches, so it is impossible to judge whether the output is better, faster, or more stable than standard practice. The paper does not contradict itself or invent entities, but it also provides no evidence that the implementation is correct or useful beyond the authors' assertion. This is aimed at R users who already rely on glmnet and want one extra function for joint tuning. It has little to offer methodologists or readers looking for new ideas. I would not bring it to a reading group. I would not cite it. It does not contain enough technical substance to justify sending it out for peer review.

Referee Report

1 major / 3 minor

Summary. The manuscript announces the R package ensr, which extends the glmnet package to perform a joint search over the elastic-net tuning parameters λ and α in order to identify an optimal pair, rather than selecting λ conditionally on a fixed α.

Significance. A reliable, documented implementation that jointly optimizes the two elastic-net parameters could reduce the need for manual or sequential tuning and improve model performance in settings with correlated predictors. However, the manuscript supplies no description of the search strategy, objective function, cross-validation scheme, or any empirical validation, so the practical significance cannot be assessed from the provided text.

major comments (1)

[Results] Results paragraph: the central claim that ensr 'search[es] the λ-α space and identify[s] an optimal λ-α pair' is unsupported because the manuscript gives no information on the search algorithm (grid, optimization routine, etc.), the criterion used to declare optimality, or the cross-validation procedure. Without these details the procedure is not reproducible and its statistical properties cannot be evaluated.

minor comments (3)

[Abstract] Abstract, Results sentence: 'enser' is a typographical error for 'ensr'.
[Abstract] Abstract, Results sentence: 'glment' is a typographical error for 'glmnet'.
[Abstract] Abstract, Motivation sentence: 'optional elastic net model' should read 'optimal elastic net model'.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and the opportunity to respond. The manuscript is a short announcement of the ensr package rather than a full methodological paper, but we agree that additional details are needed to support the central claim.

read point-by-point responses

Referee: [Results] Results paragraph: the central claim that ensr 'search[es] the λ-α space and identify[s] an optimal λ-α pair' is unsupported because the manuscript gives no information on the search algorithm (grid, optimization routine, etc.), the criterion used to declare optimality, or the cross-validation procedure. Without these details the procedure is not reproducible and its statistical properties cannot be evaluated.

Authors: We agree with the referee that the manuscript as written does not describe the search algorithm, optimality criterion, or cross-validation procedure, leaving the central claim unsupported. The ensr package implements a discrete grid search over user-specified α values (defaulting to a sequence from 0 to 1), and for each α it calls glmnet::cv.glmnet to select λ by minimizing the cross-validated loss (typically deviance). The (λ, α) pair with the overall minimum CV error is returned. We will revise the manuscript by adding a Methods section that documents the grid construction, the objective function, the CV scheme (including the number of folds and any parallelization), and the selection rule. This revision will make the procedure reproducible from the text alone. revision: yes

Circularity Check

0 steps flagged

No circularity; software announcement with no derivations or self-referential claims

full rationale

The document is a brief software announcement for the ensr R package. It contains no equations, no fitted parameters presented as predictions, no uniqueness theorems, no ansatzes, and no self-citations that support any load-bearing claim. The central statement simply describes the package's purpose and availability; nothing reduces to its own inputs by construction. This is the normal case of a non-circular methods/software note.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The document is a software package announcement and introduces no mathematical free parameters, axioms, or invented entities; it relies on the pre-existing glmnet package.

pith-pipeline@v0.9.0 · 5707 in / 963 out tokens · 45798 ms · 2026-05-25T11:10:18.051749+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

ensr extends the functionality of glmnet to search the λ−α space and identify an optimal λ−α pair
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

minimum mean cross-validation error

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.