pcalc weibull com

August 4, 2025

PCalc: Weibull Was the Pocket App for Stats Geeks. And Then It Vanished.
If you ever needed to run a Weibull or Poisson distribution on your phone without diving into spreadsheets or full-blown stat software, PCalc: Weibull was that rare app that did one thing really well—and then disappeared almost overnight.

What Was PCalc: Weibull?

Forget the fancy branding. PCalc: Weibull wasn't the high-powered, all-purpose PCalc scientific calculator from TLA Systems. This was something much simpler and more specific. Built by Jelena Bogdanovic, it launched in October 2024 as a free iOS app for iPhones and iPads. The entire pitch? Fast, accurate statistical distribution calculations on the go.

We're talking two core functions:

Cumulative Poisson Distribution
Weibull Distribution Calculations

No clutter, no bloat. Just stats.

If you’ve worked in reliability engineering, supply chain modeling, or data-heavy manufacturing, you know why this matters. Say you’re predicting machine failure rates based on historical maintenance intervals. You’re not opening up Excel on your phone. You want quick probabilities from shape and scale parameters. That’s what this app handled.

Why It Mattered

It carved out a niche few others even attempted. The Poisson side let users determine how likely it was that a discrete number of events happened in a fixed window—like, say, how often defects occur on a line per hour if your λ (rate) is 2.3. Input your rate, hit the button, done.

The Weibull calculator was gold for anyone modeling lifespans of systems or components. Think aerospace engineers checking component failure probabilities. Or warranty analysts modeling customer risk exposure. You gave it the shape (β) and scale (η) parameters, and it spit out reliability probabilities. Not “just math”—useful math with real consequences.

Apps that do this on desktop? Sure. MATLAB, R, Python, even Excel. But on a phone? Most apps drown you in extra features or don’t do this at all. PCalc: Weibull went lean and laser-focused. Around 110MB in size. Lightweight, clear, and purpose-built.

Who Actually Used It?

Probably not a ton of casual users. This wasn’t the kind of app you stumble upon. It was built for a very specific audience: people who already knew what a Weibull distribution was and needed it calculated yesterday. If you didn’t work with exponential decay, reliability modeling, or queue theory, this wasn’t for you.

There were no flashy tutorials or gamified onboarding. It expected the user to bring the knowledge. It just brought the speed.

So, What Happened?

Short answer: it disappeared.

Longer answer: version 2.0 landed in mid-October 2024, with tweaks to keyboard input and accessibility. But by early November—barely a month later—it was gone. Appfigures listed it as “removed from the store,” and download links stopped working. No blog post. No Twitter goodbye. Just gone.

There’s no official statement, but a few common culprits make sense:

Low engagement – Niche app, no App Store ratings, zero user reviews.
Monetization – It was free. No premium tier, no ads worth mentioning. Probably not sustainable.
Compliance or app policy issues – Apple’s App Store is strict, especially on outdated SDKs or unclear data practices.

Some third-party mirrors tried to offer download links, but none were verified. It wasn’t long before even those disappeared.

Is There Anything Like It Today?

Not really, not in the same way. There are alternatives—but none quite as lean.

WolframAlpha can do both distributions, but it’s tied to subscriptions and needs internet access.
Scientific calculator apps like RealCalc or HiPER provide some stats but often miss the advanced modeling.
Spreadsheets (Excel or Google Sheets) work—but aren’t quick on mobile and aren’t intuitive for people used to formulas like WEIBULL.DIST(x, alpha, beta, TRUE).
Programming notebooks like Juno (Python) let you use scipy.stats.weibull_min, but that’s a full IDE experience, not a tap-and-go solution.

The truth is, there’s a gap for this kind of ultra-focused stats app that doesn’t try to be everything. That’s exactly what PCalc: Weibull did right.

Why This Kind of App Still Matters

Mobile apps aren’t just for lifestyle or games. There's a real demand for scientific and technical utilities—especially those that don’t require an internet connection or a login. PCalc: Weibull hit that note.

In regulated industries—like defense, aviation, or medical devices—calculating failure rates or uptime probabilities isn’t a bonus skill. It’s required. Being able to run a reliability estimate while walking through a plant, on your phone, without opening your laptop? That saves time and prevents errors.

And with Weibull modeling becoming more common in software reliability and machine learning, the need for tools like this will only grow.

Why the "Weibull" Distribution?

Quick primer for the uninitiated.

The Weibull distribution is like a Swiss Army knife for failure modeling. It adapts to increasing, decreasing, or constant failure rates. Engineers love it because it tells them not just if something will fail, but how likely it is to fail over time—and under what kind of stress.

When β = 1, it's an exponential distribution (constant failure rate). When β > 1, failures increase over time—think metal fatigue. When β < 1, failures decrease over time—like early burn-in issues in electronics.

So it’s flexible, powerful, and essential in any field where durability matters. Which is a lot of fields.

FAQs

Was PCalc: Weibull available on Android?
No. It was iOS-only, and there’s no record of an Android version.

Was it related to the full PCalc app?
No. Different developer, different app. The name was just confusingly similar.

Is there a way to get it now?
Not through official stores. It was pulled from the App Store around November 2024. No verified IPA or APK downloads exist.

Why was it free?
No idea. Maybe a side project. But that might also be why it didn’t last.

Any good replacements?
WolframAlpha, statistical Python scripts via Juno or Pythonista, or custom Excel templates. But none offer the same tap-to-calculate simplicity.

Final Thoughts

PCalc: Weibull didn’t try to be everything. It was small, specific, and effective—two statistical tools wrapped in a clean interface. For professionals who needed fast, accurate distributions without distractions, it was a hidden gem. Now it’s gone, and no app has really stepped up to replace it.

If someone rebuilt it today, with support for log-normal or exponential distributions, maybe a few export options and dark mode, it could hit the mark again. Until then, the stats crowd is back to spreadsheets and code notebooks.

And that’s a shame. Because sometimes, all you want is to punch in two numbers and get an answer. No friction. No noise. Just results.