Package: rumenGP 0.2.1

Introduction | Load Example Data | Import ANKOM Data | Validate Metadata | Process ANKOM Data | Validate Processed Data | Visualize Raw Gas Production | Fit Kinetic Models | Summarize Model Fits | Identify Potentially Problematic Bottles | Plot Model Fits | Plot Residuals | Compare Models | Rank Models | Compare Models by Treatment | Rank Models by Treatment | Determine the Best Model per Treatment | Model Win Frequency | Quality Control Workflow | Available Models | Model Equivalence | Groot and Generalized Michaelis-Menten | Groot, Generalized Michaelis-Menten, and Log-logistic | [V(t) | Recommended Model Selection Workflow | New Additions | Burr XII | VF\left[1 | Inverse Paralogistic | Next Steps | Importing Manual Datasets | Importing Pressure Data | User-Defined Models | Summary

Introduction | Importing Gas-Volume Data | Required Columns | Importing Pressure Data | Pressure Units | Headspace Volume | Headspace Units | Negative Pressure Values | Validation | Fitting Models | Comparing Models | Model Ranking | Available Models | Model Equivalence | Groot and Generalized Michaelis-Menten | Groot, Generalized Michaelis-Menten, and Log-logistic | $$V(t) | Recommended Model Selection Workflow | Common Errors | No Gas or Pressure Supplied | Both Gas and Pressure Supplied | Missing Headspace Volume | Summary | Next Steps

Introduction | Understanding Common Parameters | Asymptotic Gas Production | Fermentation Rate | Lag Time | Half-Time Parameters | Shape Parameters | Groot | Generalized Michaelis-Menten | Log-logistic | Burr XII | Inverse Paralogistic | Interpreting Dual-Pool Models | Rapid Fraction | Slow Fraction | Understanding Goodness-of-Fit Metrics | R-Squared | Important | RMSE | RSS | AIC | BIC | Why Higher R² Does Not Always Mean a Better Model | Understanding Model Equivalence | $$V(t) | Interpreting Burr XII and Inverse Paralogistic Models | [V(t) | VF\left[1 | Model Selection Strategy | Interpreting Failed Fits | Biological Reality Matters | Practical Recommendations | Use Simple Models When | Use Lag Models When | Use Flexible Sigmoidal Models When | Use Multi-Pool Models When | Summary

Introduction | Single-Pool Models | Brody | Equation | [V(t) | A\left(1 | Parameters | Advantages | Limitations | Ørskov and McDonald | EXP0 | EXPL | Gompertz | A\exp\left[ | Logistic | Mitscherlich | A\left[1 | \exp\left(-k(t-\lambda) | d\left(\sqrt | LE0 (Logistic-Exponential Without Lag) | LEL (Logistic-Exponential With Lag) | \frac{A\left(1-e^{-k(t-\lambda)}\right)}{1+\exp\left[\ln\left(\frac{1}{d}\right) | Generalized Michaelis-Menten | Groot | Burr XII | VF\left[1 | Parameter Constraints | Interpretation | Typical Applications | Literature Context | Inverse Paralogistic | Multi-Pool Models | Dual Logistic | Model Equivalence | Groot and Generalized Michaelis-Menten | Groot, Generalized Michaelis-Menten, and Log-logistic | $$V(t) | Choosing a Model | Simple Models | Lag Models | Flexible Sigmoidal Models | Custom Models | Model Selection Guidance

Introduction | Example Dataset | Example 1: Simple Exponential Model | Example 2: Hyperbolic Model | [Gas(t) | Example 3: Richards Model | Comparing Custom and Built-in Models | Visualizing Custom Models | Choosing Starting Values | Using Bounds | Best Practices | Summary