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+title: kaefa
+description: Automated exploratory factor analysis for R, with model search, item-fit diagnostics, and multilevel workflows.
+theme: jekyll-theme-cayman
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+# kaefa
+
+<a href="https://deepwiki.com/ContextualWisdomLab/kaefa">
+  <img
+    src="https://deepwiki.com/badge.svg"
+    alt="Ask DeepWiki"
+  >
+</a>
+
+**kaefa** is an R package for automated exploratory factor analysis (AEFA).
+It helps researchers explore uncertain factor structures, compare candidate
+models, evaluate item fit, and iterate toward better-fitting solutions,
+including workflows for complex and multilevel data.
+
+## Product surfaces
+
+- **kaefa-core** — the statistical engine behind `aefa()` and `engineAEFA()`,
+  including candidate-model search, information-criterion selection, item-fit
+  evaluation, and theta-prior utilities.
+- **kaefa-studio** — the bundled Shiny interface launched with `launchAEFA()`
+  for researchers who prefer an interactive workflow.
+- **Remote execution** — optional worker initialization through `aefaInit()`
+  for analyses that outgrow a local workstation.
+
+The package remains R/`mirt` based. Current architecture and supported
+boundaries are documented in the repository rather than inferred from
+experimental branches.
+
+## How AEFA works
+
+The current AEFA workflow explores multiple candidate factor structures and
+item-response models, selects among fitted candidates using information
+criteria, checks item-level fit, removes poorly fitting items one at a time
+when appropriate, and re-estimates until the search converges. AIC is the
+default model-selection criterion; AICc, BIC, sample-size-adjusted BIC, and
+posterior DIC when a fitted model actually provides it are also supported.
+
+## Install
+
+Install the organization-owned repository directly from GitHub:
+
+```r
+# install.packages("devtools")
+devtools::install_github("ContextualWisdomLab/kaefa")
+```
+
+Then run a basic analysis:
+
+```r
+library(kaefa)
+fit <- kaefa::aefa(mirt::Science)
+fit
+```
+
+For the interactive interface:
+
+```r
+library(kaefa)
+launchAEFA()
+```
+
+## Documentation and onboarding
+
+- [Repository README](https://github.com/ContextualWisdomLab/kaefa) —
+  installation, examples, remote execution, workload sizing, Shiny usage, and
+  quality information.
+- **Architecture**
+  <https://github.com/ContextualWisdomLab/kaefa/blob/develop/ARCHITECTURE.md>
+  — runtime flow, product boundaries, repository layout, and quality gates.
+- [Source repository](https://github.com/ContextualWisdomLab/kaefa) — issues,
+  pull requests, releases, code, and current development activity.
+- [Releases](https://github.com/ContextualWisdomLab/kaefa/releases) — packaged
+  release history when available.
+- [Ask DeepWiki](https://deepwiki.com/ContextualWisdomLab/kaefa) —
+  repository-aware questions about the codebase and documentation.
+
+## Research and model-evidence boundary
+
+kaefa is a research-oriented statistical package. Model fit, convergence,
+item-fit diagnostics, and parameter-recovery evidence should be interpreted in
+the context of the data-generating process and the chosen IRT/factor model.
+New claims about supported model classes or recovery quality belong in
+reviewed code, tests, and traceability documentation before they are presented
+here as released capability.
+
+## License
+
+kaefa is distributed under the GNU General Public License v3.0. See the
+repository for the authoritative license text and current source.