NEWS

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ecoGLMM 0.1.4

• Added preflight data-quality checks to the generic analysis template, including response-family validation, non-finite-value detection, constant predictor detection, and summaries of missing and unique values.
• Added an optional predictor-correlation screen to the template. Because the current candidate set fits one environmental predictor per model, the screen reports potentially redundant ecological hypotheses without applying VIF or removing variables automatically.
• Added exports for the data-quality summary, predictor correlations, and the complete preflight-check object.
• Added an automated test ensuring that the installed generic template exists, is non-empty, and can be parsed by R.
• Restored automatic export of a multi-page DHARMa_diagnostics.pdf, with the residual panels and a formal-test summary for every selected response model.

ecoGLMM 0.1.3 (2026-09-30)

• Published on CRAN on 30 September 2026.

• Assigned the permanent CRAN DOI 10.32614/CRAN.package.ecoGLMM.

• Standardized the complete real-data workflow and documentation in English.

• Added complete author metadata and ORCID identifiers for André Felipe Carneiro dos Santos, Bruna Martins Bezerra, and Bárbara Lins Caldas de Moraes.

• Restored MuMIn::r.squaredGLMM() as the primary R-squared method to match the original dissertation pipeline.

• Added parallel Nakagawa R-squared results from performance::r2_nakagawa() in explicitly labelled columns.

ecoGLMM 0.1.2

• Corrected likelihood-ratio-test degrees of freedom to report the difference in model parameters rather than the total parameter count.
• Removed unused colour and fill labels from additive effect plots.

ecoGLMM 0.1.1

• Corrected the Authors@R role field in DESCRIPTION.

ecoGLMM 0.1.0

• Initial research release.
• Fits additive and predictor-by-period GLMM candidate sets.
• Supports beta, Gamma, Gaussian, Poisson, and negative-binomial families.
• Calculates AICc, Akaike weights, evidence ratios, and competitive-model flags.
• Compares nested additive and interaction models with likelihood-ratio tests.
• Extracts coefficients, Wald confidence intervals, and Nakagawa R-squared.
• Runs DHARMa uniformity, dispersion, and outlier diagnostics.
• Produces marginal-effect, model-selection, and coefficient figures.
• Exports analysis tables and figures.