Recording GLMM Software and Package Versions

Why record the software environment?

A GLMM result depends on more than the model formula and data. The R version, the versions of packages used to fit and summarize the model, and aspects of the operating environment can affect whether the analysis runs and whether its results agree closely with a previous run. Recording those details makes it possible to investigate discrepancies and gives another analyst a concrete starting point for reproducing the workflow.

A version record is not a substitute for the analysis itself. Reproduction also requires the analysis code, the data (or a clear, lawful way to obtain them), and the choices that affect computation, such as random seeds where random procedures are used. Nor can a package lockfile guarantee bit-for-bit identical results on every computer. It records important software dependencies; it does not capture every operating-system library, hardware feature, or external input.

What to record with a fitted analysis

For an R GLMM workflow, keep a record of at least the R version, the versions of the packages used for fitting and reporting, the operating system or platform, and the project’s dependency lockfile. If the model was fitted with `glmer()`, for example, record the version of `lme4`; also record versions of packages used for data preparation, diagnostics, or result extraction. A record of `lme4` alone does not describe the full analysis environment.

R’s `sessionInfo()` is a convenient session-level summary. It reports the R version and platform, locale information, and packages attached or loaded in the session. It is useful as a snapshot, but it is not a complete inventory of every package installed on the computer. In a project using `renv`, `renv.lock` records package dependencies and versions for the project library. Save both: the session record documents the actual run, while the lockfile is intended to help rebuild the project library.

A practical record also identifies which script produced the fitted object, the analysis date, and the input data file or data release. These details connect the environment to a specific analysis rather than to an unspecified computer session. Keep records with the project, under version control where appropriate, and avoid storing confidential data or credentials in them.

Recording and restoring project dependencies with renv

`renv` manages a project-specific R library and stores a lockfile, usually named `renv.lock`, in the project directory. A typical workflow initializes `renv` for the project, installs and uses the needed packages there, and then records the project’s dependencies. The key commands are `renv::init()` to initialize a project, `renv::snapshot()` to update the lockfile, `renv::status()` to check whether the project library and lockfile agree, and `renv::restore()` to install the versions specified in the lockfile.

For a project whose package dependencies are discoverable from its code, a typical sequence is `renv::init()`, run the analysis and install any needed packages, then run `renv::snapshot()`. The lockfile should be reviewed and committed with the analysis code. A collaborator who obtains the project can run `renv::restore()` to recreate the project library. In a scripted restore, `renv::restore(prompt = FALSE)` suppresses the confirmation prompt; it does not remove the need to inspect changes before running it in an important environment.

Package discovery depends on how the project is organized. If package use is indirect or occurs in code that `renv` does not discover, the analyst should check that the required packages are represented in the lockfile rather than assuming that every package used was captured. `renv::status()` helps reveal differences between the project library and the lockfile, but it cannot determine whether the analysis script has omitted a dependency or whether the recorded analysis is scientifically appropriate.

Why a lockfile helps: a short justification

Suppose a project uses packages \(P_1,\ldots,P_k\), and each package may depend on other packages. Recording only the directly named packages is insufficient if a dependency changes and alters installation or behavior. A lockfile records the package versions and dependency information needed to reconstruct the project library, so restoration can use the recorded dependency set instead of selecting whatever versions happen to be current.

This can be stated as a simple dependency argument. Let \(D\) be the set of packages required by the project, including dependencies, and let \(v(P)\) denote the recorded version of package \(P\). A successful package-level restoration aims to install each \(P \in D\) at its recorded version \(v(P)\). If the analysis instead uses unrecorded current versions, then for at least one package \(P\), the installed version may differ from \(v(P)\); identical project code alone does not ensure the same package environment. This is why code and a lockfile complement one another. The argument guarantees neither identical numerical output across platforms nor identical results if the input data or analysis code differ.

Restoration, verification, and practical failure modes

Restoration is a procedure with prerequisites. The collaborator needs the project files and lockfile, a compatible R installation, access to the required package sources, and any operating-system libraries needed to install packages from source. A package may no longer be available from its original repository, or a package requiring compiled code may depend on system tools or libraries that are absent. These are reasons restoration may fail even when the lockfile is correct.

After restoration, verify rather than assume. Check `renv::status()`, compare the R version with the project record, and save a new `sessionInfo()` file. Then run the analysis script from its documented starting point and check that it completes. For a model fit, compare relevant outputs at an appropriate precision, while recognizing that small numerical differences can occur across operating systems, numerical libraries, or hardware. If outputs differ materially, investigate the data, code, package versions, random-number settings, and platform instead of attributing the discrepancy to one cause without evidence.

Common alternatives have different strengths. A manually written package list is quick to read but can omit transitive dependencies or exact versions. `sessionInfo()` is valuable evidence of a run but does not, by itself, recreate the project library. A lockfile supports package restoration but does not fully specify the operating system or every external library. A container can capture more of the operating environment, but it adds build and maintenance requirements and still depends on preserving the container definition and its inputs. These methods can be combined; none makes the analysis independent of its data and code.

Common mistakes and limitations

• Recording only “R 4.3.” A minor-version label is not an exact R version. Record the full version string, such as R 4.3.2, and the platform information available from the session.
• Assuming `sessionInfo()` is a lockfile. It documents the active session but is not a complete specification for restoring a project library. Keep a project lockfile as well.
• Assuming the lockfile captures everything. It records package dependencies, not every operating-system component, compiler, external data source, or hardware detail. Note relevant system requirements when they affect installation or computation.
• Saving only the fitted object. A serialized fit does not preserve the complete analysis process. Retain code, data provenance, and environment information, and do not assume that an object created in one package version will always load identically in another.
• Calling a restoration exact without checking. A successful installation is not proof that the R version, all dependencies, or the operating environment match. Verify the restored project and report any differences.

The practical goal is a transparent, auditable workflow, not a promise that every computer will produce bit-for-bit identical output. In a GLMM report, identify the software used to fit the model and preserve the detailed environment record with the analysis materials. This gives future analysts the information needed to reproduce the workflow, assess discrepancies, and distinguish a software mismatch from a change in the data or analysis.