Changes for version 0.296 - 2026-08-09
- fixed CPAN bug: https://www.cpantesters.org/cpan/report/fcf32c68-75a5-1014-bc87-8fe0d10910fe
- write_table.announce.t ran its child perl through -e, which cannot carry double quotes or shell metacharacters on Windows; the child program now goes in a file
- chisq_test now matches R 4.6.1 bit-for-bit on the statistic across 170 randomized cross-check cases, and the full suite (116 files, 18,546 tests) passes.
- Bugs found and fixed in LikeR.xs
- 1. A 1×k or k×1 table returned df = 0, p = 1 — no test at all. R collapses a single-row/column matrix to a vector and runs goodness-of-fit (if (min(dim(x)) == 1L) x <- as.vector(x)); now so does this. [[10,20,30]] went from X²=0, df=0, p=1 to X²=10, df=2, p=0.006738. 2. Yates' label was attached even when the correction was zero. R only says "with Yates' continuity correction" when min(0.5, |O−E|) > 0. A table sitting exactly on its expectation, and every zero-margin table, were mislabelled. 3. Yates was computed per cell instead of as R's single whole-table min(0.5, abs(x-E)) — equal in theory on a 2×2, not always in the last bits. 4. No input validation. Negatives, infinities, NaN, strings and undef were silently coerced to 0 and produced garbage or NaN; all-zero data returned NaN; a single element returned df = 0. All now croak with R's wording. Ragged array rows and 2D hash rows with mismatched column keys were silently zero-filled — now fatal. 5. Uniform expectation used n/k instead of R's n * (1/k), and sums were accumulated in a plain NV where R uses a long double. Together these put the statistic 1–2 ulp off R on most inputs; both fixed (ct_acc_t). 6. Hash input was read in Perl's randomized key order, so which row a malformed hash got blamed on was a coin toss. Rows and columns are now sorted, as fisher_test already does. 7. Segfault on sparse arrays (av_fetch returns NULL for a hole) — this one I introduced during the rewrite and caught before finishing; guarded by ct_av_get
- Three of those cross-checks compared the statistic to R's printed value relatively, and on the tables in question R's value is not a statistic. Where a 2×2 has all four |O−E| equal, Yates' min(0.5, |O−E|) cancels every corrected residual, so the exact statistic is 0 and the exact p is 1; what R prints there — 1.4515367733818938e-24 for [[1573,3],[4,0]], 2.9347503914472165e-32 for [[1,2],[3,4]], 7.1842689582627857e-32 for [[1.5,2.5],[3.5,4.5]] — is the leftover of forming E in floating point, the four |O−E| differing in their last bits so that the minimum comes out a hair below the rest. Its size is a property of the NV rather than of the test: a double build reproduces R's digits, and a __float128 build cancels the whole way to 0. Comparing that relatively can only pass on the width R happened to use, and it failed with rel diff = 1 on the quadmath perl and on 5.12.5. Those three cases in t/chisq_test.R.scipy.t now check the statistic against 0 and the p-value against 1 with absolute tolerances of 1e-20 and 1e-11, R's numbers staying in the file as provenance. LikeR.xs is unchanged — the wide-NV answer was the more accurate one. The suite passes on perl 5.10.1, 5.12.5, 5.42.3-thr, 5.44.0 and 5.44.0-quadmath.
Modules
Get basic statistical functions, like in R, but with Perl using XS for performance