Systematic theory development: a rigorous, reproducible workflow for building, developing and testing scientific theories. This is the feature-parity twin of the Python package of the same name. The two implementations produce identical verdicts and byte-identical diagram intermediate representations (see ?theoryforge for the shared specification behind that guarantee).
Interactive web app
Run the package in your browser, with no installation, using the interactive web app. It executes the real package client-side via webR: load a theory, run any operation, and export both the visualisation (SVG/PNG) and the R code to reproduce it.
Installation
remotes::install_github("pablobernabeu/theoryforge", subdir = "r/theoryforge")From a local checkout, the package also installs as source:
# from the repository root
install.packages("r/theoryforge", repos = NULL, type = "source")The package depends on yaml and jsonlite.
Quick start
library(theoryforge)
# Read a bundled example theory (or build one incrementally with tf_theory + tf_add_*)
theory <- tf_read(system.file("fixtures", "panic-network.theory.yaml",
package = "theoryforge"))
tf_validate(theory)
# Score it against the 12-item rigour checklist
report <- tf_check(theory)
report$aggregate_score # 84.8
report$gate # "pass"Get started walks through building, checking and diagramming a theory. Developing and testing continues into severity, preregistration, amendment appraisal and the audit dossier, and Mapping the literature positions a theory within a bibliometric corpus.
Public API
The reference index lists every exported function, grouped by workflow stage. tf_render_diagram() renders any digraph view in the viewer through DiagrammeR, or as a standalone SVG string with as = "svg"; the rendering packages are optional, in Suggests, so the deterministic core stays dependency-free. For the rationale behind each rigour check and exactly how every reported value is computed, see Methodological foundations.
Licence
MIT. See LICENSE.