kobaltGIT

It's not about memory safety per se. It's about not losing an hour of analysis because a malformed .sav file or a zero-variance sample triggered a segfault or silent wrong output. SPSS crashes on bad data. Researchers work with messy data. Rust's Result type makes "handle every error explicitly" the default, not an afterthought. That's why I chose it.

I'm building StatCraft — an open-source statistical analysis package in 100% Safe Rust, as a clean-room alternative to IBM SPSS Statistics.

I started this because I believe statistical software should not crash on malformed data, should be scriptable by AI agents via MCP, and should not cost thousands of dollars per year.

Current state (11 days in, solo):

- 14 statistical blocks audited against SPSS 24 Algorithms Guide with ≤1e-6 SciPy precision

- Full .sav/.zsav codec, Excel/CSV import, SPSS syntax parser (.sps)

- 37 SPSS-style dialogs, pivot tables, Output Viewer with TSV export

- Built-in MCP server for Claude/ChatGPT/Ollama agents

- Zero unwrap/expect/panic in production; fuzzed on corrupted inputs — 0 panics

What I need help with:

- Testers with real SPSS datasets (the weirder the encoding, the better)

- Rust developers for the expression evaluator (3 known precedence gaps)

- Statisticians to validate output against their SPSS workflows

- Anyone who wants to port a procedure (regression, factor, reliability are in)

GitHub: https://github.com/kobaltgit/StatCraft