Package: biogrowth 1.0.8

The serial-fold dilution method | Principles | Implementation of the serial dilution method | Getting the time-to-detection | Estimating the growth rate from the time-to-detection | Estimating both the growth rate and lag phase duration from the time-to-detection

Theoretical background | The fit_coupled_growth() function | Two-steps fitting of the Baranyi-Ratkowsky model | One-step fitting of the Baranyi-Ratkowsky model | References

Population growth in predictive microbiology | Primary growth models included in biogrowth | Modified Gompertz model under static conditions | Logistic growth model | Richards growth model | Baranyi model under dynamic conditions | Baranyi model under static conditions | Relationship between Q0 and the lag phase duration | Baranyi model under static conditions without stationary phase | Baranyi model under static conditions without lag phase | Trilinear model under static conditions | Bilinear model with lag phase | Bilinear model with stationary phase | Loglinear model | Gathering primary models metadata directly from biogrowth | Secondary growth models included in biogrowth | Gamma factors by Zwietering | Cardinal Parameter Model (CPM) | (Adapted) Full Ratkowsky model | Secondary model by Aryani | Secondary model for water activity by Rosso | Secondary model for inhibitory compounds | Gathering meta data about secondary models directly from biogrowth | Summary of the model parameters | Numerical methods | References

Growth predictions with uncertainty using predict_growth_uncertainty() | Accounting for the uncertainty of a fitted model using predictMCMC() | Accounting for uncertainty when calculating the time to reach a population size

Models fitted under constant environmental conditions | Models fitted under dynamic environmental conditions | Growth models fitted to multiple experiments (global fit) | Fitting of secondary models

Fitting growth models with fit_growth() | Fitting primary growth models | Fitting both primary and secondar models from a single experiment under dynamic environnental conditions | Fitting both primary and secondary models from several experiments (global fitting) | Fitting secondary growth models with fit_secondary_growth()

The function predict_growth() | Growth predictions under constant environmental conditions | Basic growth predictions | Alternative definitions of model parameters | Definition of different logarithmic bases | Growth predictions under dynamic environmental conditions

Combining plots into subplots | Saving and reshaping plots | Full manual control

Growth rate under constant environmental conditions | Mathematical justification | Implementation in biogrowth | Unit interpration for model fitting | Dynamic environmental conditions | A note when comparing predictions based on primary models against models with secondary models

Deterministic modelling | Growth prediction under static conditions | Growth prediction under dynamic conditions | Time to reach a given population size | Fitting of primary models | One-step fitting under dynamic conditions | Global fitting of various (dynamic) experiments | One-step fitting using MCMC algorithms | Global fitting using MCMC algorithms | Stochastic prediction based on an MCMC fit