Product Decisions This Supports
- Exploratory data prototypes in PHP-only stacks: php-r can justify a short spike or internal tool where a team already runs PHP and wants quick vector math, rolling means, or NA-aware summaries without standing up a Python/R service. It informs "build a small internal helper vs. add a new runtime" decisions for low-stakes analysis.
- Build vs. buy for analytics primitives: The package is a reference implementation of R semantics (recycling rules,
NA propagation, vectorized arithmetic). It is useful as a specification when deciding whether to build a thin vector/statistics layer in-house or adopt a mature library — compare its API surface against your requirements rather than adopting it directly.
- Feature scoping for reporting/ETL modules: If your roadmap includes lightweight in-app aggregation (mean/median/stddev over result sets, element-wise transforms on aligned arrays), this package clarifies which operations are genuinely small to implement and which (recycling, NA handling) carry subtle correctness costs worth budgeting for.
- Roadmap signal — not a strategic dependency: With an archived repo and a last release in December 2017, it is best treated as prior art and a demo of feasibility, not as a component to schedule into a product plan. Do not let it anchor a roadmap commitment to "R-style analytics inside PHP."
- Use cases it fits: internal dashboards, one-off data cleanup scripts, teaching vectorized thinking to PHP developers, and evaluating whether your domain actually needs vector semantics before investing in a real data stack.
When to Consider This Package
Consider it only when all of the following hold:
- The work is internal, disposable, or exploratory — a spike, a notebook replacement, a script — where a rewrite later is acceptable.
- Your team is PHP-only by constraint, and adding a Python/R service or a polyglot runtime is disproportionate to the task.
- Correctness requirements are modest: you can tolerate the documented R recycling behavior and the package's own
NA semantics without independent verification.
- You have vendored or pinned the source and accepted ownership of it, since the upstream is archived and will receive no fixes.
- Data volumes are small and in-memory — vectors, not datasets; this is not a query engine or a streaming processor.
- You need only basic descriptive statistics and element-wise math, not regression, distributions, time-series modeling, or anything requiring numerical stability guarantees.
Look elsewhere when:
- You need maintenance, security patching, or PHP 8.x compatibility guarantees. An archived 2017 package is a liability in any production path; verify runtime compatibility yourself before even prototyping.
- Performance or scale matters. A pure-PHP vector library will not compete with native arrays in extension-backed tooling, let alone NumPy/pandas.
- Numerical accuracy is a product requirement (financial, scientific, regulated reporting) — you want a maintained, tested statistics library.
- You can call out to a service. A small Python/Julia/R sidecar, or PHP's own
ext-stats-style options and modern maintained packages, will be a better long-term bet.
- You need active community support, documentation depth, or a security review trail.
How to Pitch It (Stakeholders)
For executives: This is a small, MIT-licensed PHP library that mimics R's vector math. It is not a candidate for production adoption — the project is archived and has not been released since 2017. Its value to us is as a low-cost feasibility reference: it shows that basic vectorized statistics can be expressed in plain PHP, which helps us size the "do we need a separate data service?" question before we commit engineering budget. Recommendation: fund a short, time-boxed evaluation using it as prior art, not as a dependency; keep the production analytics decision open.
For engineering: php-r demonstrates R-style recycling, element-wise arithmetic, and NA-aware descriptive statistics in lightweight, Composer-installable PHP. Treat it as a read-only reference implementation and prototyping aid. Expect no upstream fixes, no PHP 8 validation, and no security maintenance. If a prototype proves out, plan to either reimplement the narrow slice of vector/statistics behavior we actually use — with our own tests around recycling and missing-value semantics — or move the workload to a maintained service. Do not vendor it into a production dependency graph without an explicit ownership decision and a compatibility spike.
Bottom line for the room: useful as a proof of concept and design reference; unsuitable as a platform commitment. Decide on the analytics direction on its own merits, not on this package's availability.