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predmicror 1.3.2

• Improve predmicror_assistant() with a deterministic model registry, wrapper-first code generation, static code validation, and return_trace diagnostics.
• Make the assistant fall back to registry-based output when Ollama is unavailable instead of failing immediately.
• Bundle inst/shiny/predmicror-assistant/app.R and extend predmicror_assistant_app() with model, root, host, port, and browser arguments plus a fallback app.
• Add assistant tests for registry metadata, deterministic output, and Shiny app bundling.
• Add data-aware assistant support for data frames and uploaded .csv, .tsv, .xls, and .xlsx files, including automatic profiling, column detection, and data-specific wrapper code.
• Improve the assistant Shiny app with a card-based layout, separate Answer/Code/Data/Trace views, local Ollama model selection, and manual task/column overrides.
• Add initial dynamic modelling support with dynamic_profile(), RK4-based predict_dynamic_growth(), predict_dynamic_inactivation(), and finite-difference dynamic_sensitivity().
• Add dynamic fitting wrappers fit_dynamic_growth() and fit_dynamic_inactivation() with predmicror_dynamic_fit methods and diagnostics.

predmicror 1.3.1

• Polish the GitHub README to reflect the current fitting, diagnostics, and model-comparison API.
• Add explicit package website and issue tracker links to the README.
• Make applied inactivation and cardinal-model vignettes more robust by using explicit fitted/residual column access.
• Group applied workflow articles in the pkgdown articles index.

predmicror 1.3.0

Documentation and applied workflows

• Added an applied vignette for microbial inactivation models using fit_inactivation(), predmicror_augment(), fit_metrics(), and compare_models().
• Added an applied vignette for cardinal parameter models using fit_cardinal(), diagnostics helpers, and model comparison tools.
• Expanded examples showing safer post-fitting workflows and prediction over new data grids.

predmicror 1.2.1

• Register default S3 methods for predmicror_augment() and fit_metrics() in roxygen documentation.
• Add the pkgdown site URL to DESCRIPTION.
• Add the new fitting and diagnostic topics to the pkgdown reference index.
• Ignore temporary phase overlay folders created while applying local hotfixes.

predmicror 1.2.0

• Add predmicror_augment() to extract original data, fitted values, residuals, model name, and model type from predmicror_fit objects.
• Add as.data.frame.predmicror_fit() as a lightweight base-R shortcut for predmicror_augment().
• Add fit_metrics() to calculate residual diagnostics and information criteria for fitted models, including SSE, RMSE, MAE, bias, residual standard error, R2, adjusted R2, log-likelihood, AIC, BIC, and convergence status.
• Add compare_models() to combine diagnostics across multiple fitted models and sort by AIC, BIC, RMSE, or MAE.
• Add tests for diagnostic extraction, model metrics, and model comparison.
• Add a model-comparison vignette showing how to compare alternative fitted predictive microbiology models.

predmicror 1.1.3

• Declare shiny as a suggested package for assistant functions.
• Import utils::tail() for assistant history formatting.
• Keep R CMD check free of errors and warnings, apart from environment-specific timestamp notes.

predmicror 1.1.0

• Add fit_growth(), fit_inactivation(), and fit_cardinal() wrappers around gslnls::gsl_nls().
• Add predmicror_models() to list wrapper-supported models and required starting parameters.
• Add the predmicror_fit class with print(), summary(), coef(), fitted(), residuals(), predict(), plot(), vcov(), logLik(), AIC(), and BIC() methods.
• Add input validation for data columns and starting values in the fitting wrappers.
• Update README content, package metadata, .Rbuildignore, and CI workflow templates.
• Complete the WeibullMM() example and fix the HuangNLM() example label.
• Configure testthat edition 3.

predmicror 1.0.1

• Fix Rosso full model parameter order and Baranyi reduced formulation.
• Add numeric stability guards with log1p(), expm1(), and bounded square roots across growth and cardinal models.
• Expose the Richards shape parameter and align examples with log10 inactivation scales.
• Correct dataset documentation and row counts.
• Add testthat coverage for growth, cardinal, and inactivation models.

predmicror 1.0.0

• First release.
• Primary growth models.
• Inactivation models.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.