youngchanresearcher.r-universe.dev

[youngchanresearcher] SCAtools 0.5.1

youngchanresearcher@gmail.com (Young Chan) — Tue, 13 Oct 2026 14:20:02 GMT

Tests whether a high or low level of a condition is enough to guarantee a high or low level of an outcome. Such a claim predicts that one corner of the condition-outcome scatter plot is empty of observations. The package estimates the boundary of that empty corner, its size as a share of the scope (the effect size), a permutation test against random pairing, and a threshold table giving the condition level that guarantees each outcome level, in actual units, percentages, percentiles or standard deviations. Each of the four directions (high or low condition, high or low outcome) is stated as a claim and mapped to its empty corner. Because a condition can rule out low outcomes while barely moving the average, results can be set beside ordinary least-squares and median regression slopes, showing whether the data hold an average effect, a sufficiency effect or both. Also provides plots, influential-case diagnostics, scope-sensitivity and resampling checks, random-data generation and power analysis for planning studies. Frontiers are estimated with the 'NCA' package.

[youngchanresearcher] NSCA 0.4.5

youngchanresearcher@gmail.com (Young Chan) — Sat, 10 Oct 2026 13:51:48 GMT

Tests and quantifies bivariate statements in which a condition is both necessary and sufficient for an outcome. Necessity and sufficiency are estimated as two empty-space frontiers on diagonally opposite corners of the same scatter plot, using the 'NCA' package of Dul (2016) for the necessity side and 'SCAtools' for the sufficiency side. The two empty zones and the region between them tile the analytic scope, which yields the reported areas as one identity rather than as separate definitions: a weakest-component effect, a normalised geometric mean of the two components, the share of the scope the claims jointly rule out, and the data zone they still admit. The conjunction is decided by an intersection-union combination of the two directional permutation tests, optionally driven by a single shared permutation sequence so that a statistic of both components can also be tested. These are random-pairing screens rather than direct tests of a causal necessary-and-sufficient relation, no magnitude benchmarks are asserted for the joint indices, and a dual-threshold table separates outcome levels that are out of reach, admitted, or guaranteed. An ordinary least-squares line can be drawn alongside the two frontiers as a central-tendency reference; it is an average-effect summary and is never treated as a component of either claim.

[youngchanresearcher] CORAtool 0.1.2

youngchanresearcher@gmail.com (Young Chan) — Fri, 25 Sep 2026 20:13:02 GMT

Searches configurational data for causes that are each an insufficient but non-redundant part of an unnecessary but sufficient (INUS) condition for their effect, so that cause-effect relations are marked by conjunctivity and disjunctivity. The method, Combinational Regularity Analysis (CORA), borrows its Boolean minimisation algorithms from switching circuit analysis. Truth tables are minimised either with the classical Quine-McCluskey algorithm over positive and don't care terms or with McCluskey's modified algorithm over positive and negative terms, and the resulting prime implicant charts are solved with Petrick's method. Multi-value conditions and structures with simple as well as complex effects are supported, together with a configurational data-mining search and two-level logic diagrams. The package is an R port of the 'Python' packages 'CORA' and 'LOGIGRAM' described in Sebechlebská, Mkrtchyan and Thiem (2023) ; it computes in plain R and requires no 'Python' installation. It is an independent implementation and is not endorsed by the authors of the original packages.