[yosleycarrero2025] wordorientation 0.1.0

you@example.com (Your Name) — Thu, 27 Aug 2026 18:51:01 GMT

Provides tools to quantify how strongly pairs of words attract or repel each other in a text corpus, based on co-occurrence patterns. For each word pair, the phi coefficient (a correlation measure for binary variables) is computed from a document-term matrix and tested for significance, then classified as showing attraction (co-occurring more than chance would predict), repulsion (co-occurring less than chance would predict), or no significant relationship. A full pipeline is provided from raw text to a labeled network visualization. Unlike general-purpose pairwise correlation tools, 'wordorientation' is built specifically for text: it handles tokenization and stopword removal, applies significance-based classification rather than reporting a raw correlation coefficient alone, and produces a ready-to-plot attraction/ repulsion network. Methods for word co-occurrence and the phi coefficient follow Evert (2008) and standard corpus linguistics practice for collocation and association measures.

[yosleycarrero2025] cochranSize 0.1.0

c3046338@newcastle.ac.uk (Yosley Carrero) — Sun, 02 Aug 2026 21:13:03 GMT

Provides functions to calculate the minimum required sample size for surveys and studies using Cochran's formula, including the finite population correction. Cochran's formula is a standard method in survey methodology for determining sample size based on a desired margin of error, confidence level, and (optionally) known population size. All parameters (margin of error, confidence level, and expected proportion) are fully adjustable by the user rather than fixed to any convention.

yosleycarrero2025.r-universe.dev

[yosleycarrero2025] wordorientation 0.1.0

[yosleycarrero2025] cochranSize 0.1.0