Skip to content

Roadmap

0.1 — foundation

  • direct fuzzy calibration, logistic and piecewise
  • crisp calibration
  • typed fuzzy-set expressions
  • necessity/sufficiency parameters of fit
  • complete binary truth tables
  • exact classical QMC
  • conservative and parsimonious csQCA/fsQCA solutions
  • directional intermediate solutions
  • pandas-native result objects
  • parity harness against R QCA

0.2 — parity and robustness

  • standard intermediate-solution simplifying-assumption algorithm
  • enhanced necessity analysis and supersets/subsets
  • contradictory simplifying assumptions
  • solution-specific unique coverage
  • calibration diagnostics
  • XY plots
  • extend the R-QCA golden parity suite (calibration, truth tables, fit measures and conservative/parsimonious solutions are already covered as of 0.1; remaining: intermediate solutions, necessity supersets, multi-outcome models)
  • optional R-compatible calibration snapping, so extreme memberships can be reported exactly as R does when replicating an existing analysis

0.3 — performance

  • faster bitset/cube minimiser
  • prime-implicant consistency filters
  • row dominance
  • optional Rust acceleration

1.0

  • tQCA
  • stable public API
  • benchmark corpus
  • exhaustive cross-software validation
  • published algorithm and software paper