projectLSA 0.1.1

CFA/SEM Module (major update)

• Added an Advanced Analysis tab supporting Multi-group CFA (MGCFA) with configural, metric (weak), scalar (strong), and strict invariance levels, plus a one-click Auto Invariance Test that fits and compares all levels.
• Added Latent Growth Modelling (LGM) via lavaan::growth().
• Added built-in example datasets for the new modes: PoliticalDemocracy (SEM) and Demo.growth (LGM).
• Added a Data Summary panel with descriptive tables, data-type composition, and a missingness/correlation heatmap.
• Added Calculate Variable: build composite (mean/sum) scores from a simple NewVar = var1, var2, ... syntax, both for the analysis data and for new data.
• Added Score New Data: download an Excel template, upload new cases, and compute factor scores from the fitted model.
• Added a Methodological Guide with the reporting conventions used by the module.
• Added editable model-comparison table, Heywood-case status flags, and an option to use robust/scaled fit indices.
• Reworked the parameter output into a single tab covering factor loadings, regression paths, variances, and covariances.
• Path diagram gains a Display Scope option (full model vs. structural model only).

Save & Restore Analysis Sessions

• Every module (CFA, EFA, LTA, LPA, LCA) can now save the whole analysis session to an .rds workspace file and restore it later by uploading that file as the data source. The workspace stores the data, the fitted model objects, and the relevant input settings.

HTML Reports for Every Module

• CFA/SEM, EFA, LTA/IRT, LPA, and LCA each gain a Report Preview tab: render the report in-app first, then download the finished HTML.
• Reports embed the R console output and, when requested, the AI interpretive summary.

AI Assistant (new)

• Added a floating AI Assistant widget available in every module, with three tabs: Ask AI (conversational Q&A about the current results), Create Summary (manuscript, APA 7, paragraph, bullet, or table format, in English or Indonesian), and Settings.
• Supports Google Gemini, OpenAI, Groq, and OpenRouter. The API key is supplied by the user at runtime, is never stored by the package, and no request is made unless the user provides one.
• The assistant is grounded with reporting rules of thumb for mixture models (Nylund et al., 2007; Masyn, 2013), IRT, and CFA/SEM (Hu & Bentler, 1999; Hair et al., 2017; DiStefano & Morgan, 2014).
• Optional research context can be typed in or uploaded (txt/pdf/docx) to tailor the interpretation, and the generated summary can be pushed into the HTML report.
• The widget can be docked as a tab inside the active module or floated, and the chat transcript can be exported as HTML.

LTA/IRT Module

• Added an Information & Reliability tab (test information, marginal reliability, conditional standard error of measurement).
• Added a DIF Analysis tab with group and anchor-item selection.
• Added Score New Data with an Excel template download.

EFA Module

• Added data summary visualisations, composite-variable calculation, and scoring of new data.

Other

• Added a floating R Console widget showing the raw R output behind the results.
• Report previews are now written to a per-session directory and served under a per-session resource prefix, removed when the session ends. Previously every module wrote to a fixed path inside tempdir() and published the whole of tempdir() over HTTP; because tempdir() is shared by all sessions served by one R process, concurrent users of a hosted deployment could overwrite and read each other’s reports.
• New runtime dependencies: httr, jsonlite, shinycssloaders, writexl. semTools and pdftools are optional (Suggests).

projectLSA 0.0.9

CFA/SEM Reporting Module

• Added HTML report generation for CFA and SEM models via R Markdown, including model summary narrative, fit index comparison tables, and side-by-side path diagrams (initial vs. final model).
• Report automatically detects whether the fitted model is CFA or SEM based on the presence of regression paths (~ operator).
• Report supports configurable decimal separator (period or comma) for international conventions.

Path Plot Enhancements

• Added five new color palettes: Vibrant, Monochrome, Sunset, Rose, and Mint.
• Decimal separator setting now also applies to edge labels on the path diagram.

UI & UX Improvements

• Citation copy and download buttons aligned and centered on the homepage.
• New runtime dependencies added to Imports: kableExtra, knitr, magick, officer, rmarkdown, scales.

projectLSA 0.0.8

• Removed dependency on the semTools package.
• Re-implemented Average Variance Extracted (AVE), Composite Reliability (CR), and Heterotrait–Monotrait Ratio (HTMT) internally using lavaan-based computations.
• The HTMT implementation follows the default HTMT2 formulation (geometric mean) and produces results identical to semTools::htmt().
• No changes to the public API or user-facing outputs.
• Minor internal refactoring of the Shiny server logic for CFA diagnostics.

projectLSA 0.0.7

• Minor refinements to the Shiny application codebase to support the new exploratory functionality

projectLSA 0.0.6

• Extended and enhanced the Latent Class Analysis (LCA) and Latent Profile Analysis (LPA) modules with additional tools for in-depth result exploration.
• Added advanced exploratory features to support deeper inspection of class/profile characteristics, model outputs, and interpretation workflows.
• Improved interactivity and usability of LCA and LPA result views within the Shiny application.
• Internal refinements to the Shiny application codebase to support the new exploratory functionality.
• No changes to the public API or core estimation procedures.

projectLSA 0.0.5

• Documentation updated and expanded, including README.Rmd, README.md, and rendered README.html.
• Figures reorganized and updated to improve clarity of methodological workflows.
• Minor refinements to package metadata and CRAN submission-related files.
• No changes to the public API or core analytical functionality.

projectLSA 0.0.3

• Initial release to CRAN.
• Includes a full Shiny-based graphical user interface for:
  • Latent Profile Analysis (LPA)
  • Latent Class Analysis (LCA)
  • Latent Trait Analysis (LTA / IRT)
  • Exploratory Factor Analysis (EFA)
  • Confirmatory Factor Analysis (CFA)
• Includes interactive visualizations, downloadable outputs, and built-in example datasets.
• Provides run_projectLSA() as the main entry point for launching the application.