Overview | Part 1: Zotero Setup & API Credentials | 1. Create a Group Library | 2. Generate an API Key | Part 2: Dynamic URL Parameters | Parameter Reference | Constructing the URL | Part 3: Embedded Apps & User Workflows | 1. The Full Overview App (Standalone) | 2. The Embedded Reader App (LMS / OPAL / Iframe) | Testing & Executable Examples

1. Getting Started via App Link | 2. Exploring and Interacting with the Library | 3. Enriching Metadata | 4. Changing Collections & Exporting | 5. Editing References and Viewing PDFs (Zotero Workflow)

Introduction | Installation of Keras 3 in R | 1. Install the R Package | 2. Environment Setup by Operating System | Linux (Ubuntu / Debian / Fedora) | Windows | Example Workflow: Non-Linear Synthetic Regression | Step 1: Preprocessing Machine and Data Splitting with mlfeaturer | Step 2: Building and Compiling the Keras Model | Step 3: Model Training | Step 4: Model Evaluation on Scaled Space | Step 5: Back-Transformation and Evaluation on Physical Scale | Acknowledgments | References

Introduction | Core Objectives | Target Audience & Use Cases | Installation | Example Workflow: Preprocessing Microalgae Growth Data | The dauta4 Dataset | Step 1: Scaling and Transformation Pipeline | Step 2: Extracting Preprocessing States | Step 3: Visualizing Relationships Across Preprocessing States | Step 4: Model Fitting and Prediction Back-Transformation | Step 5: Evaluating Model Predictions on Physical Scale | Summary & Next Steps | Acknowledgments | References

Introduction | Installation | Prepare Data | Fit a Neural Network | Evaluate the Network | Graphical Comparison of Results | Acknowledgments | References

A simple ODE: chaos in the atmosphere | Solvers for initial value problems of ordinary differential equations | Partial differential equations | Differential algebraic equations | Integrating systems containing complex numbers, function zvode | Making good use of the integration options | Events and roots | Delay differential equations | Discrete time models, difference equations | Plotting deSolve Objects | Troubleshooting | References

Introduction | A simple ODE example | Alternative way of passing parameters and data in compiled code | deSolve integrators that support DLL models | Testing functions written in compiled code | Using forcing functions | Implementing events in compiled code | Delay differential equations | Difference equations in compiled code | Final remark | References

Background | Setup and Model Compilation | Setting Time, Parameters, and Initial Conditions | Optional: Cloning and Altering Parameters for Comparison | Running Simulations | Visualizing Results and Diagnostics | References

Preface | Introduction | Exercises | Exercise 1 | Exercise 2 | Exercise 3 | Exercise 4 | Acknowledgments | References

Introduction | State variables | State equations | Inputs | Scenarios | Examples | Series 1: realistic time series with balanced inflow and outflow | Example 2: Simplified time series with a summer flood

The Basics | Fitting Parameters | Implementing Models in Compiled Languages | Troubleshooting | References

Introduction | Design goals | Approach | Two introductory examples | Implementation of simecol models | Creating own models | Discussion | References

Introduction | Pre-requisites | Using the package | More options | Further information | References

Introduction | Pre-requisites | Using the package | More options | Further information | References

Introduction | Methods | Installation | Data set | Estimation of growth rates | Easy Linear Method | Parametric nonlinear growth models | Differential equation models | Selective parameter fitting | Nonparametric smoothing splines | Fit multiple data sets without loops | Fit smoothing splines to multiple data | Fit parametric models to multiple data | Dose Respone Curves | Nonparameric Spline Fits | Parametric Baranyi Model | Acknowledgments | References

Acknowledgments | References

Introduction | Methods | Data set | Identification of the wild-type population | Kernel density smoothing | ECOFFinder | Mixture approach | Fiting multiple data sets | Finally | Acknowledgments | References

Introduction | Constants and datasets | Physical functions | Dissolved gasses | Seawater properties | Conversions | Finally

Introduction | The class "proto" and its methods | Examples | Summary | References | Frequently Asked Questions | Reference Card

A steady-state model of oxygen in a marine sediment | Global sensitivity analysis : Sensitivity ranges | Local sensitivity analysis : Sensitivity functions | Fitting the model to the data | Running a Markov chain Monte Carlo | Finally

Introduction | The test model | Local sensitivity analysis | Multivariate parameter identifiability | Fitting the model to data | MCMC | Model prediction | Monte Carlo applications | Discussion | The Fortran version of the model

Introduction | The example model | Global sensitivity | Local sensitivity | Multivariate sensitivity analysis | Fitting the model to data | Markov chain Monte Carlo | Distributions | Examples | Finally

Introduction | Function modMCMC | Sampling from a normal distribution | Sampling from a lognormal distribution | The banana | A simple chemical model | Fitting a nonlinear model | Finally

Fitting a Monod function | Finally