Scope | Dependencies | Function signatures | Inputs | Outputs | FAQ | Why not use reticulate?

Introduction to Zarr | Storage size comparison | Decoding speed comparison

Introduccion | Matriz de compatibilidad | Instalacion de paquetes complementarios | ECH -- Uruguay | EPH -- Argentina | CASEN -- Chile | PNADc -- Brasil | CPS -- Estados Unidos | ENIGH -- Mexico | DHS -- Internacional | Portabilidad de recipes | Proximos pasos

¿Por qué Recipes? | Ciclo de Vida de una Recipe | Carga de una Encuesta | Construcción de una Recipe a partir de Steps | Documentación de Recipes | Validación | Aplicación de Recipes a una Encuesta | Categorías | Certificación | Publicación y Descubrimiento de Recipes | Publicar en el Registry Público | Exploración y Búsqueda | Explorador Interactivo | Registry Privado para Instituciones | Buenas Prácticas | Próximos Pasos

Why Recipes? | Recipe Lifecycle | Loading a Survey | Building a Recipe from Steps | Recipe Documentation | Validation | Applying Recipes to a Survey | Categories | Certification | Publishing and Discovering Recipes | Publishing to the Public Registry | Browsing and Searching | Interactive Explorer | Private Registry for Institutions | Best Practices | Next Steps

Introducción | Configuración inicial | Tipos de diseño muestral | Diseño ponderado simple | Diseño estratificado por conglomerados | Inspección del diseño | Múltiples tipos de ponderadores | Ponderadores de réplicas bootstrap | Configuración del motor y el procesamiento | Motor de datos | Ejecución de steps y procesamiento diferido | Comportamiento de copia | Estimación de varianza | Varianza basada en el diseño | Estimación por dominios | Razones | Validación del pipeline | Verificación paso a paso | Validación cruzada con el paquete survey | Visualización del pipeline | Evaluación de calidad | Validación de recipes | Lista de verificación para validación | Buenas prácticas | Próximos pasos

The ECH harmonization problem | STATA vs metasurvey: Side-by-side comparison | Loading real ECH microdata | Step 1: Demographic variables | Step 2: Labor force classification | Step 3: Income variables | Step 4: Geographic classification | Building the recipe | Automatic documentation | Publishing to the registry | Estimation with workflow() | Domain estimation | Quality assessment | Reproducibility: same recipe, different edition | For STATA users: quick reference | Key differences | Data and acknowledgments | Next steps

What is a Workflow? | Setup | Basic Estimation | Mean | Total | Multiple Estimates at Once | Domain Estimation | Quality Assessment | RecipeWorkflow: Publishable Estimates | Creating a RecipeWorkflow | Publishing to the Registry | Finding Workflows Associated with a Recipe | Sharing via the Remote API | Full Pipeline | Provenance: Data Lineage | Publication-Quality Tables | Next Steps

El problema de la compatibilización de la ECH | STATA vs metasurvey: Comparación lado a lado | Carga de microdatos reales de la ECH | Paso 1: Variables demográficas | Paso 2: Clasificación de la fuerza laboral | Paso 3: Variables de ingreso | Paso 4: Clasificación geográfica | Construcción del recipe | Documentación automática | Publicación en el registry | Estimación con workflow() | Estimación por dominio | Evaluación de calidad | Reproducibilidad: mismo recipe, distinta edición | Para usuarios de STATA: referencia rápida | Diferencias clave | Datos y agradecimientos | Próximos pasos

¿Qué es un Workflow? | Configuración inicial | Estimación básica | Media | Total | Múltiples estimaciones a la vez | Estimación por dominio | Evaluación de la calidad | RecipeWorkflow: Estimaciones publicables | Creación de un RecipeWorkflow | Publicación en el registro | Búsqueda de workflows asociados a un recipe | Compartir mediante la API remota | Pipeline completo | Provenance: Linaje de datos | Tablas con calidad de publicación | Próximos pasos

Introduction | Compatibility matrix | Installing companion packages | ECH -- Uruguay | EPH -- Argentina | CASEN -- Chile | PNADc -- Brazil | CPS -- United States | ENIGH -- Mexico | DHS -- International | Recipe portability | Next steps

Introduction | Initial Setup | Sampling Design Types | Simple Weighted Design | Stratified Cluster Design | Design Inspection | Multiple Weight Types | Bootstrap Replicate Weights | Engine and Processing Configuration | Data Engine | Step Execution and Lazy Processing | Copy Behavior | Variance Estimation | Design-Based Variance | Domain Estimation | Ratios | Pipeline Validation | Step-by-Step Verification | Cross-Validation with the survey Package | Pipeline Visualization | Quality Assessment | Recipe Validation | Validation Checklist | Best Practices | Next Steps

A tales object | Checking for trouble | Looking at the arrays | Where to next | These four articles form one analysis

Our research question | How does ocean3d represent three-dimensional space? | Getting started: preparing our data | Installing ocean3d | Step 1: Creating our study area and bathymetry | 1.1. Defining our study area | 1.2. Representing the seafloor | Step 2: Creating our environmental data | 2.1. Defining our depth levels | 2.2. Generating synthetic temperature data | 2.3. Creating our SpatVoxel | 2.4. Visualising our 3D environmental data | Step 3: Creating our species' three-dimensional distribution | 3.1. Defining our species' geographical distribution | 3.2. Combining the polygon, depth limits and bathymetry | Step 4: Converting our species' distribution into a SpatVoxel | 4.1. Converting our SpatEnvelope | 4.2. Visualising our species' potential occupancy | Step 5: Exploring environmental conditions within our species' potential distribution | 5.1. Extracting temperature within our species' distribution | 5.2. Exploring temperature at a specific depth | 5.3. Exploring the vertical temperature profile | Step 6 (EXTRA - come back and check it out): Converting our results back into a SpatEnvelope | 6.1. Identifying suitable temperature ranges | 6.2. Understanding the limitations of this conversion | Summary | Next steps: What else can you do with ocean3d? | Apply the workflow to real-world datasets | Explore the full package documentation | Feedback and contributing | Contact us

R and CPU Comparison | CPU-Parallel versus GPU | End-to-End GPU Fit | Host/Device Transfer Overhead

Overview | Step 1 — Ingest and prepare data | Step 2 — Select extreme-event days | Step 3 — Fit marginal distributions | Step 4 — Estimate spatial dependence | Step 5 — Fit dependence models | Copula models | Stochastic field model | Step 6 — Simulate pseudoscenarios | Step 7 — Validate with cluster-size statistics | Summary | References

Introduction | Setup | A working example

Introduction | A working example | Limitations of repeated observations models | References

Introduction | Computation time scaling | Improving computation times | References

Overview | Setup | Fit the model | Ancestral states on the latent scale | Visualizing latent ancestral states on the tree | Joint latent-space scatterplot | Ancestral states on the response scale | Pie chart tree: response-scale probabilities | Working with raw posterior draws | Identifying nodes on the tree | Selecting specific nodes and variables

Introduction | A working example

Introduction | A working example

Introduction | A working example | References

Introduction | The generalized dynamic phylogenetic model | A working example | Available response distributions | Conclusion | References

Introduction | A working example

Introduction | Default priors | Limitations on setting priors

Overview | Supported Features on GPU | Checking GPU Availability | Installing GPU Support | Using the GPU Backend | Performance Guidelines | Fallback Behaviour

Quick Decision Guide | Batch Adapter | When to Use | Example | Streaming Adapter | Parameters | Merge Strategies | Online Adapter | Update Modes | Feature Comparison

Overview | Confidence Intervals | Prediction Intervals | Both Intervals | Confidence Levels | Bootstrap Intervals | Standard Errors | Availability

Basic Smoothing | With Confidence Intervals | Handling Outliers | Streaming Mode | Next Steps

Overview | Basic Trend Extraction | Detrending | Forecasting with Prediction Intervals | Handling Missing Data | Multi-Scale Analysis | Gene Expression Time Course | Choosing Fraction for Time Series | See Also

Overview | Reproducing stats::lowess() | Why the defaults differ | What this package adds

Overview | K-Fold Cross-Validation | Leave-One-Out (LOOCV) | Seeded Randomization | Comparison | CV Metrics | Interpreting Results | Availability | Best Practices

Overview | Weighted Average (Default) | Average | Take First | Take Last | Choosing Chunk Size and Overlap | Choosing a Strategy

Overview | Methylation Profile Smoothing | The Challenge | Solution | ChIP-seq Signal Smoothing | Application | Large Genome Coverage (Streaming) | Best Practices for Genomic Data | See Also

A depth level stands for a slab, not a knife edge | Touching is not overlapping | Where the slab edges fall: bounds

References

Acquiring Your TERN API Key | Saving Your API Key Locally (using {keyring}) | Reading TERN Data

Notation | Matrix calculus reminders | First-order matrix calculus | $$\begin{aligned}\frac{\partial\det(\boldsymbol{A})}{\partial p}&=\det(\boldsymbol{A}),\mathrm{tr}!\left(\boldsymbol{A}^{-1}\frac{\partial\boldsymbol{A}}{\partial p}\right),\\frac{\partial(\boldsymbol{U}\boldsymbol{A}\boldsymbol{V})}{\partial p}&=\boldsymbol{U}\frac{\partial\boldsymbol{A}}{\partial p}\boldsymbol{V},\\frac{\partial\boldsymbol{A}^{-1}}{\partial p}&= | $$\frac | \mathrm | $$\frac{\partial}{\partial p}\bigl((\boldsymbol{y}-\boldsymbol{x}p)^{\top}\boldsymbol{\Omega}(\boldsymbol{y}-\boldsymbol{x}p)\bigr) | Second-order matrix calculus | $$\frac | \boldsymbol | $$\frac | Inner products in the score | Unconstrained score and Hessian | $$\ell_{\boldsymbol{y}\mid\boldsymbol{\zeta}}(\boldsymbol{p}) | \tfrac{1}{2}\log\det\boldsymbol{\Omega}(\boldsymbol{p}) | The determinant term carries a factor $\tfrac{1}{2}$ | What “unconstrained” means here | Unconstrained gradient | Unconstrained Hessian | $$\begin | \underbrace | \boldsymbol | $$\begin | \underbrace | \boldsymbol | Constrained optimisation on the simplex | Log-sum-exp and softmax | $$s_ | \frac | e^{z_{i}-\operatorname{LSE}(\boldsymbol{z})},\qquad\operatorname{LSE}(\boldsymbol{z}) | $$H_ | s_{i}\bigl[(\delta_{ij}-s_{j})(\delta_{ik}-s_{k}) | Isometric log-ratio map | $$\boldsymbol | \mathbf | \mathbf | \operatorname{softmax}(\mathbf{V}\boldsymbol{\rho}) | ILR is a $C^{2}$ diffeomorphism | Why ILR is the default chart | Efficient derivatives of $\boldsymbol | $$\mathbf{J}_{\boldsymbol{\psi}}(\boldsymbol{\rho}) | \frac | $$\mathbf | Change of variables: ambient $\boldsymbol | Constrained score and Hessian | $$\nabla_{\boldsymbol{\rho}}\ell | \mathbf{J}{\boldsymbol{\psi}}^{\mathsf{T}}\nabla{\boldsymbol{p}}\ell | $$\mathbf | Implementation map | Expected Fisher information and Wald inference | Unconstrained expected Fisher information | $$I(\boldsymbol | Observed versus expected information | Constrained case: ILR delta method | $$I_ | \mathbf | Wald is the cheapest, not the safest, interval | Numerical speed-ups and solver safeguards | Cache a Cholesky factorisation of $\boldsymbol{\Sigma}(\boldsymbol{p})$ | Guard the box-constrained L-BFGS-B path | marqLevAlg Hessian sign under minimize = FALSE | Model objects and S3 accessors | Numerical consistency of the likelihood and its derivatives | Density against mvtnorm | Richardson extrapolation of the score and Hessian | Expected information versus Monte Carlo (\mathbb{E}[-\mathbf{H}]) | Structure-aware covariance backends | Decision tree | Computational complexity | Appendix: additive log-ratio chart | $$p_ | \frac | \frac | Why ALR is not the default | Structural zeros and folded simplex models | References

Motivation | Using grumpy | Structured datatypes

NumPy format versions | Data types | Dimensions | Pickled objects

Generative model | Factorial design (972 scenarios) | Scenario ID | Log-likelihood along the simplex | Inference | Visualisations | Other one-sample log-likelihood slices (not drawn) | Likelihood geometry and solver behaviour | What is being optimised | One bulk, $N=1$ | Reading the twelve meta-scenarios | What the heatmaps still add | See also | References

How Robustness Works | Robustness Methods | Bisquare (Default) | Huber | Talwar | Comparison | Detecting Outliers | Scale Estimation | Auto-Convergence

Introduction | How this differs from a single optimal cut-point | The data | 1. Data preparation | 2. How many cut-points? | 3. Where are the boundaries? | 4. Are the boundaries stable? | Reading the tier | 5. Results at 500 replicates | 6. Reporting | Group composition | Hazard ratios and diagnostics | Joint stability | 7. Conclusion | Reference

Introduction | The data | 1. Setup | 2. Data preparation | 3. Finding the cut-points | How many? | Where? | 4. Stability | Results at 500 replicates | 5. Reporting and diagnostics | Group composition | Hazard ratios | Proportional hazards | Landmark analysis | Survival curves | 6. Conclusion | References

Overview | Data | Empirical bivariate transition counts | Global bivariate case | Local pattern identification for a complete case | Repeating the analysis from each perspective | Reading the global and local steps together | References

Purpose of the package | Real-world use cases | Data structure | Estimation and identification | Exported functions | Assumptions and current scope | Relationship to the workflow vignettes | References

Overview | Data | Univariate worked example | Bivariate worked example | Sequence-length sensitivity results | Interpretation | References

Overview | Data | Empirical transition counts | Maximum-likelihood transition probabilities | Univariate pattern identification | Interpretation | References

1. Baselines: comparing against your current production model | 1.1 Visual comparison | 1.2 Numeric comparison: sa_top_candidates_table() and sa_compare() | 2. Providing calendar regressors (trading days, moving holidays) | 2.1 Precomputed regressors (example: length-of-month) | 2.2 Moving-holiday pulses via build_user_xreg() (Genhol-style) | 2.3 SEATS and “future” xreg values (why padding exists) | 3. Decision rules: existence of seasonality & switching models | 3.1 “DO_NOT_ADJUST” vs “adjust” | 3.2 “Keep current model” vs “switch to new model” | 3.3 Putting it together: a minimal decision wrapper | 4. Further reading

Built-in Easter handling | Precomputed trading-day regressors | Moving holidays with build_user_xreg() | Inspecting the final decision

How this package differs from alternatives | Setup | A first seasonal adjustment with seasonal | Low-level diagnostics: sa_tests_model() | Automatic model search: auto_seasonal_analysis() | Calendar regressors (trading days & moving holidays) | Inspecting the winning model | Visual “cards”: sa_existence_card() and sa_engine_choice_card() | From analysis to decision: sa_should_switch() | A full HTML report in one call: sa_report_html() | Where to go next

2. Part I – Internationally common recommendations | 2.1 When is seasonal adjustment appropriate? | 2.2 Building models that are parsimonious and stable | 2.3 Calendar effects: trading days and moving holidays | 2.4 Stability, revisions and model review | 2.5 Documentation, transparency and reproducibility | 3. Part II – Implementing the recommendations with seasight | 3.1 Deciding whether to seasonally adjust | 3.2 Parsimonious and stable models | 3.3 Calendar effects: trading days, Easter, and user xregs | 3.3.1 Supplying calendar regressors via td_candidates | 3.3.2 A practical note on SEATS and future xreg values | 3.3.3 Inspecting what won (and why) | 3.4 Stability, revisions and model review | 3.5 Documentation, transparency and reproducibility

Overview | Online Mode: Point-by-Point | Sensor Data Example | Streaming Mode: Chunk Processing | Log File Processing | Real-Time Dashboard Example | Choosing Parameters | Online Mode | Streaming Mode | Performance Considerations | See Also

WCS Basics | EMODnet WCS Services | Available services | Connecting to EMODnet WCS Services | Getting Metadata | Downloading Coverages | Further resources

How Custom Weights Work | When to Use Custom Weights | Custom Weights vs. Robustness Iterations | Basic Usage | Suppress a Known Outlier | Emphasize Important Points | Propagate Measurement Uncertainty | Combined with Robustness Iterations | Validation Rules | See Also

What is LOWESS? | How It Works | The Fraction Parameter | Robustness Iterations | Confidence vs Prediction Intervals | Execution Modes | Quick Decision Guide | Key Advantages | Next Steps

Overview | MAD — Median Absolute Deviation (Default) | MAR — Median Absolute Residual | Mean — Mean Absolute Residual | Choosing a Scaling Method

Overview | Available Kernels | Tricube (Default) | Epanechnikov | Gaussian | Biweight | Cosine | Triangle | Uniform | Choosing a Kernel

Introduction | Input data | Bundled example data | Key outputs | Data Status Check | Ecological Report | Working with the camReport object | A modular reporting workflow | Reproducibility, interpretation, and scope | Relationship to other camera-trap packages | Conclusion

Patterns | Matches | Phases and Contexts | Phases | File Contexts | Code Contexts | Rule-based | Computed

Introduction | Formant Extraction Methods | Available Methods | Method Comparison | 1. Burg's Method (Standard) | When to Use Burg | 2. Keep All Method | When to Use Keep All | Keep All vs. Burg Example | 3. Alternative Methods (Advanced) | Choosing Formant Ceiling | Speaker-Specific Ceilings | Guidelines | Parameter Tuning | Window Length | Time Step | Number of Formants | Troubleshooting Formant Detection | Problem: No Formants Detected | Problem: Formants Jump Erratically | Problem: F3 Detected as F2 | Vowel Space Analysis | Extracting F1-F2 for Vowel Plot | Formant Tracking Over Time | Advanced: Formant from Excitation | Best Practices Summary | General Recommendations | Quality Checks | Session Info

Introduction | Key Concepts | Cochleagram Analysis | Creating a Cochleagram | Understanding the Bark Scale | Querying Cochleagram Values | Visualizing Cochleagrams | Advanced: Ear-Drum-Brain Model | Excitation Patterns | Creating Excitation from Spectrum | Creating Excitation from Cochleagram | Perceptual Distance | Clinical Applications | Hearing Loss Simulation | Speech Intelligibility Prediction | Loudness Recruitment Assessment | Formant Extraction from Excitation | Best Practices | Choosing Parameters | Performance Considerations | Memory Management | Comparison with Traditional Analysis | Further Reading | Session Info

Overview | Basic Usage: Single Object Plots | Sound Waveform | Pitch Contour | Spectrogram | Spectrum | Intensity | Formants | Pitch Tier | Formant Grid and Formant Tier Styles | Cepstral Analysis | Composing Multi-Layer Plots | Spectrogram with Pitch Overlay | Spectrogram with Formant Tracks | Spectrogram with Pitch and Formants | Sound with Pitch Overlay | Customization with ggplot2 | Custom Themes | Custom Colors | Faceting by Time Windows | Statistical and Alignment Tools | PCA: Scree, Scores, and Combined Plots | DTW Warping Path | Parameter Reference | Common Parameters | Spectrogram Parameters | Formant Parameters | Pitch autolayer Parameters | Comparison with plot() Methods | Saving Plots | Summary

Introduction | Installation | Creating and Loading Sounds | Generate Test Sounds | Load Audio Files | Basic Sound Properties | Sound Statistics | Pitch Analysis | Pitch Parameters | Querying Pitch Values | Formant Analysis | Speaker-Specific Settings | Querying Formant Values | Intensity Analysis | Intensity Statistics | Intensity at Specific Time | Working with Data Frames | Visualization | Complete Workflow Example | Comparing with Praat Scripts | Praat Script | pladdrr Equivalent | Advanced Topics | Custom Analysis Parameters | Handling NA Values | Package Information | See Also | Conclusion

Overview | Background | TextGrid Annotation | The Phonetic Analysis Pipeline | Part 1: Loading and Creating Data | Creating Synthetic Audio | Creating a TextGrid | Plotting the TextGrid | Part 2: TextGrid Queries | Exploring Tier Structure | Extracting Interval Information | Part 3: Sound Manipulation | Extracting Audio Segments | Resampling | Intensity Normalization | Pre-emphasis Filtering | Part 4: Acoustic Analysis | Fundamental Frequency (F0) Extraction | Formant Analysis | Intensity | Voice Quality (HNR) | Part 5: Batch Processing | Part 6: Data Export and Visualization | Export to CSV | Visualization | Real-World Applications | 1. Sociolinguistic Research | 2. L2 Acquisition Studies | 3. Clinical Voice Assessment | Best Practices | 1. Pre-emphasis for Formants | 2. Appropriate Analysis Parameters | 3. Handle Missing Values | 4. Efficient Batch Processing | Summary | References

Introduction | Key Differences | Architecture | Syntax Comparison | Common Operations | Loading Sound Files | Pitch Extraction | Formant Analysis | Intensity Measurements | Spectral Analysis | Batch Processing | Parselmouth Approach | pladdrr Approach | Working with Data Frames | Extracting Time Series Data | Visualization | Parselmouth with matplotlib | pladdrr with ggplot2 | Advanced Workflows | Voice Analysis Pipeline | Integration with Data Science Tools | Parselmouth + pandas/numpy | pladdrr + tidyverse | Advantages of pladdrr | When to Use Each | Use Parselmouth if: | Use pladdrr if: | Common Issues and Solutions | Issue 1: String-based commands | Issue 2: Positional parameters | Issue 3: Documentation | Getting Help | Conclusion

Introduction | Key Principles | 1. Object Creation | 2. Method Calls | 3. Parameters | Common Workflows | Basic Pitch Analysis | Formant Extraction | Intensity Measurements | Spectral Analysis | TextGrid Manipulation | Batch Processing | Praat Script Approach | pladdrr Approach | Advanced Features in pladdrr | Integration with tidyverse | Visualization with ggplot2 | Advantages Over Praat Scripts | Common Pitfalls | 1. Object Lifetime | 2. Time Ranges | 3. Unit Strings | Getting Help | Conclusion

Introduction | What is SIMD? | Example: Adding Two Vectors | SIMD in pladdrr | Automatic Detection | What you actually get | Measuring the effect on your hardware | What Operations Use SIMD? | Phase 1: Matrix and Data Operations | Phase 2: Intensity Analysis | Phase 3: Correlation and Windowing | Phase 4: FFT and Formant Extraction | Benchmarking Your Own Workloads | FFT Performance | Formant Extraction Performance | Cochleagram Performance | Optimization Tips | 1. Use Appropriate Sample Rates | 2. Optimize Time Steps | 3. Process Multiple Files Together | 4. Reuse Objects When Possible | 5. FFT Sizes | Platform Notes | Apple Silicon (M1/M2/M3) | Intel/AMD (x86_64) | Server CPUs with AVX-512 | Older CPUs | Profiling Your Workloads | Basic Timing | Detailed Profiling | Benchmarking Multiple Methods | SIMD Accuracy | Fallback Behavior | Compilation Notes | Installing from Source | Binary Packages | Troubleshooting | SIMD Not Detected | Not Sure Where Time Is Going? | Summary | Advanced Performance APIs | Batch Statistics API | Fast CPPS API | Adaptive Pitch Range (for VUV analysis) | XPtr Window Functions (RcppXPtrUtils) | When to Use Advanced APIs | Further Reading | Session Info

Introduction | Why Use FormantPath? | The Ceiling Frequency Problem | Standard vs FormantPath Comparison | Basic Usage | Creating a FormantPath | Interpreting num_steps_up_down | Querying FormantPath | Inspecting Candidates | Stress Values (Quality Metric) | Extracting the Optimal Formant | Automatic Extraction | Manual Path Selection | Visualizing FormantPath Results | Comparing All Candidates | Optimal Path vs Standard | Advanced Path Finding | Viterbi-Style Path Optimization | Use Cases | 1. Unknown Speaker Demographics | 2. Noisy or Difficult Audio | 3. Segment-Specific Analysis | 4. Batch Processing Multiple Files | Performance Considerations | Memory Usage | Computation Cost | Best Practices | DO | DON'T | Comparison with Other Tools | pladdrr FormantPath vs Praat | vs Parselmouth | Troubleshooting | Issue: All candidates look similar | Issue: Extracted formant has gaps (NA values) | Issue: FormantPath is too slow | Summary | Further Reading | Session Info

Introduction | Why Speech Synthesis? | Research Applications | The Klatt Synthesizer | Basic Usage | Recommended: Pre-configured Vowels | Save to File | Avoid Empty Grids | Vowel Synthesis | The Vowel Triangle | Formant Values for Common Vowels | Voice Types | Pitch Contours | Dynamic Intonation | Common Pitch Patterns | Formant Transitions | Diphthongs | Vowel Continuum | Visualizing Synthesis | Spectrogram of Synthetic Vowel | Comparing Real vs Synthetic | Advanced Features | Voice Quality Control | Complex Synthesis Example | Analysis -> Synthesis Workflow | Complete Pipeline | Use Cases | 1. Perceptual Experiments | 2. Voice Morphing | 3. Teaching Phonetics | Known Limitations | 1. Empty Grid Errors on Synthesis | 2. Limited Tier Manipulation | 3. Formant Constraints | Best Practices | Do | Don't | Troubleshooting | Error on to_sound() | Unnatural sounding synthesis | No sound output | Comparison with Other Tools | pladdrr vs Praat GUI | vs Parselmouth | vs Other R Synthesis | Summary | Further Reading | Session Info

Overview | Background | TextGrid Structure | Common Workflows | Part 1: Creating TextGrids | Creating from Scratch | Adding Interval Boundaries | Adding Point Annotations | Loading from File | Part 2: Querying TextGrids | Tier Information | Extracting Interval Information | Time-Based Queries | Point Tier Queries | Part 3: Modifying TextGrids | Inserting and Removing Boundaries | Relabeling | Adding and Removing Tiers | Duplicating Tiers | Part 4: Large-Scale Corpus Analysis | Loading Large TextGrids | Efficient Data Extraction | Sampling Large Datasets | Batch Processing Multiple Files | Part 5: Integration with Forced Alignment | Montreal Forced Aligner (MFA) Output | Quality Control for Forced Alignment | Part 6: Data Export and Visualization | Export to CSV | Save Modified TextGrid | Visualization | Best Practices | 1. Consistent Labeling | 2. Handle Empty Intervals | 3. Time Precision | 4. Backup Before Editing | Real-World Applications | 1. Prosodic Analysis | 2. Voice Onset Time (VOT) Measurement | 3. Automated Tier Creation | Summary | References

Introduction | Cepstral Analysis Visualization | Power Cepstrum | Power Cepstrogram | CPP Time Series | Comprehensive Cepstrum Report | Formant Visualization | F1-F2 Formant Space | Formant Trajectories | Pitch and Intensity Visualization | Pitch Contour | Intensity Contour | Combined Pitch and Intensity | TextGrid Visualization | Annotation Tier Display | Duration Analysis | Spectral Visualization | Spectrogram | Spectrum | Long-Term Average Spectrum (LTAS) | Customization and Theming | Custom Color Schemes | Publication Themes | Multi-Panel Figures | Saving Plots | High-Resolution Output | Batch Saving | Summary

Overview | Background | Vowel Acoustics | The F1-F2 Vowel Space | Formant Normalization | Part 1: Data Preparation | Loading Audio and Annotations | Part 2: Formant Extraction | Choosing Analysis Parameters | Pre-emphasis | Extract Formants | Part 3: Multi-Point Measurements | Part 4: Formant Normalization | Lobanov Z-Score Method | Part 5: Vowel Space Statistics | Descriptive Statistics by Vowel | Vowel Space Area | Part 6: Trajectory Analysis | Detect Diphthongization | Part 7: Visualization | Basic F1-F2 Vowel Plot | Advanced Plotting with ggplot2 | Faceted by Speaker or Condition | Part 8: Statistical Analysis | ANOVA: Vowel Differences | Mixed-Effects Models | Real-World Applications | 1. Sociolinguistic Variation | 2. L2 Acquisition | 3. Speech Clarity Assessment | Best Practices | 1. Parameter Selection | 2. Quality Control | 3. Normalization Choice | 4. Missing Data | Summary | References

pladdrr Performance Optimization Guide | The Three Performance Tiers | When do the tiers actually matter? | Tier 1: High-Level API (Best for Most Users) | Tier 2: Direct API | Fewer R→C boundary crossings | Available Direct Functions | Tier 3: Batch & Parallel API | Batch Operations | Vectorized Queries | Parallel Processing | Tier Comparison Examples | Example 1: Single File Analysis | Example 2: Batch Processing | Example 3: Formant Tracking Over Time | Decision Tree: Which Tier Should I Use? | Best Practices | 1. Start with Tier 1, Optimize Later | 2. Batch Similar Operations | 3. Reuse Pointers in Loops | 4. Choose a Core Count That Fits Your Workload | 5. Combine Batch and Parallel Processing | Troubleshooting | Parallel Processing Issues | Runtime Not Improving? | Summary Table | Further Reading

Do you need tar_target_cpp()? | The two constructors | How a C++ step differs from the Python/Julia steps | Arguments | Installing Rcpp and checking | Object output (iris example) | File output | Extension packages | Parallel computing with RcppParallel | Bringing an R function into C++ | The function is not part of the compiled library | Where the R function can be defined | Dynamic branching | Compile the C++ code only once across branches | They fall back to the plain pattern on Python and Julia | Several C++ steps in one pipeline | Compile once, reuse in other steps | Reusing a tar_target_cpp() companion library elsewhere | Using more than one library in a step | Current limitations | Step output and tar_polyglot_log() | Rtools note (Windows) | Trade-offs vs Python/Julia

pkgaudit steps | Discover | Extract | Analyze | Resolve | Call list | Invariants | Scanned files are never executed | File size limits are applied before extraction | Failures are contained and counted | Rules are integrity-checked | Every rule is verified against its own examples

Example package | Auditing a package | Examining results | File contexts | Patterns | Matches | Coverage | Errors | Metadata | Subsetting by phase | Reviewing findings | Exporting findings | Auditing a tarball | Rule database integrity

The two constructors | Arguments | Scripts, data, and output | Choosing the Julia install / project | The three-script model | Object output | File output | Dynamic branching (iris example) | Parallel computing (Distributed, not Threads) | Choosing the Julia install and project | Use case 1: Julia on PATH (default) | Use case 2: a specific juliaup version | Use case 3: an explicit Julia home | Use case 4: a pinned project (recommended) | Tracking scripts as dependencies | Conversion caveats

The two constructors | Arguments | Scripts | Wiring in data and reading it back | Choosing the interpreter / environment | The three-script model | Object output | File output | Dynamic branching | Parallel computing (multiprocessing) | Choosing the Python environment | Use case 1: global / system default | Use case 2: a pinned uv virtualenv (recommended) | Use case 3: a poetry project | Use case 4: a plain venv or a conda env | Use case 5: pin just the version | Tracking scripts as dependencies | Conversion caveats

Do you need tar_target_rs()? | The two constructors | How a Rust step differs | Arguments | Installing Rust and rextendr | Object output (iris example) | File output | Crate dependencies, features, profile | Dynamic branching | Compile the Rust code only once across branches | They fall back to the plain pattern on Python and Julia | Several Rust steps in one pipeline | Compile once, reuse in other steps | Using more than one library in a step | Current limitations | Windows toolchain note | Trade-offs vs Python/Julia

Motivation | Installation | Limitations to know first | A worked pipeline (Python + iris) | The three-script model | Object output: return a value into the pipeline | File output: return paths to files on disk | Tracking the scripts as dependencies | Parallelism and isolation with crew | Pre- and post-scripts: moving variables in and out | Sending variables in (the pre-script) | Reading variables out (the post-script) | Where to next

The three forms at a glance | 1. A literal path string | 2. A tar_target_path() reference | 3. Inline code with tar_code() | Mixing forms in one call | Which should I use? | Tracking helper files your script imports | Wiring the helper in with inputs | Julia | Why C++ and Rust are different | A note on building code with glue() | See also

Overview | Extend (Default) | Reflect | Zero | No Boundary Padding | Comparing Policies

From R-universe (recommended) | From conda-forge | From Source (GitHub) | System Requirements | Verifying Installation

Introduction | Standalone Expression Evaluation | Numeric Expressions | String Expressions | Vector Expressions | Matrix Expressions | String Array Expressions | Persistent Interpreter with State | Creating an Interpreter | Setting Variables | Getting Variables | Using Variables in Expressions | Vector and Matrix Operations | Praat Variable Type Reference | Object Management | Practical Example: Statistical Calculations | Performance Considerations | Best Practices | Summary | See Also

Rationale | Custom grid creation | Setup | Usage | Data Preparation | Using grid_depth() | Tuning | Inspect | Visualizing Results | Finalizing the Model | Evaluating on the test set | A Note on Parametric Activations

Introduction | Why Analysis-Resynthesis? | The Perceptual Loop | Applications | Workflow 1: Basic Analysis → Synthesis | Step 1: Load and Analyze Audio | Step 2: Extract Mean Formant Values | Step 3: Resynthesize with KlattGrid | Step 4: Compare Original vs Resynthesized | Workflow 2: Vowel Round-Trip Validation | Create Synthetic Vowel with Known Parameters | Analyze Synthetic Vowel | Visualize Round-Trip | Workflow 3: Vowel Space Mapping | Synthesize Vowel Triangle | Analyze Vowel Triangle | Plot Vowel Space | Verify Vowel Space Relationships | Advanced: Voice Morphing | Morph Between Two Speakers | Visualize Morph Continuum | Use Cases | 1. Formant Manipulation Experiments | 2. Pitch-Formant Decoupling | 3. Quality Assessment | Best Practices | DO | DON'T | Troubleshooting | Poor Resynthesis Quality | High Round-Trip Error | Segfault During Synthesis | Performance Notes | Memory Usage | Summary | Further Reading | Session Info

COGs are lazy until you touch the values | Look before you pull | Crop to an area of interest first | Extracting at points is already bandwidth-safe | Mind the coordinate reference system | Save the crop, then work offline

Basic usage of collect_tern_data() | Efficiently download data points at multiple spatial locations | Graceful handling of failed dataset fetches

A synthetic dataset for a grain production experiment | Download SMIPS data for the sites across times | A simple model for grain yield | Augmenting the yield model with soil moisture data | Simplified collection with collect_tern_data()

The Bridge-to-MIDAS Spectrum | A Small Monthly-to-Quarterly Example | Deterministic Bridge Aggregation | Fixed numeric weights | Unrestricted Mixed-Frequency Regression | Parametric MIDAS-Style Weighting | Forecast Comparison | Picking an Aggregator on Out-of-Sample Performance | Choosing an Aggregation Strategy | Related packages

Overview | Example Data | Stationarity expectations | A Basic Bridge Model | Lags in the target equation | Standardized Output | Forecast Visualization | Multiple Indicators | Direct Alignment | Non-Standard Calendars | Optional Uncertainty Output | Where to Go Next

Overview | Fitting a Model with Default Uncertainty | Forecast Output | Reporting Only Forecasts Below a Width Tolerance | Summary Output | Scenario Forecasting with xreg | Optional Full-System Bootstrap | Point Forecasts Only | Interpretation

Introduction | Simulating data | Model fitting | Extracting coefficients and making predictions | Modelling under complex group structures | Modelling under privileged information | Reference

Overview | Hazard groups | Risk levels and control measures | Step-by-step example | 1. Classify liquid volatility | 2. Assign the hazard group | 3. Obtain the risk level | 4. Get the recommended control measures | 5. Full assessment in one call | Evaluating multiple substances | From Excel (no coding required) | Language | Interactive application | References

Overview | The five-factor cascade | Risk bands | Step-by-step example | 1. Hazard class | 2. Quantity and frequency classes | 3. Exposure potential | 4. Volatility (liquid) | 5. Process and collective protection | 6. VLA correction factor | 7. Final inhalation risk score | 8. Risk characterisation | Full assessment in one call | From Excel (no coding required) | Language | Interactive application | References

Overview | The two-stage procedure | Stage 1: Preliminary assessment (minimum 3 measurement days) | Stage 2: Statistical assessment (minimum 6 measurement days total) | Stage 1 example | Step-by-step | Stage 2 example | Periodic assessment (monitoring interval) | Additive effects | From Excel (no coding required) | Language | Interactive application | Methodological note | References

Setup | Name parsing | Name matching | Name resolution

Background | Setup | Parsing | Matching | Takeaways

The Air Quality System (AQS) | About AQS Data Mart | Benefits of using RAQSAPI | References

Introducción | Creación de un RotativePanelSurvey | Acceso a los componentes del panel | Aplicación de pasos a los componentes del panel | Estimación sobre componentes del panel | Análisis de corte transversal (Implantación) | Comparación entre seguimientos | PoolSurvey: Estimación combinada | Estimación agrupada | Múltiples grupos | Extracción de encuestas desde paneles | Patrones temporales | Carga de datos de panel desde archivos | Pesos replicados bootstrap | Buenas prácticas | Próximos pasos

Introduction | Setup | Preparing the design for convey | Inequality Measures | Gini coefficient | Atkinson index | Quintile share ratio (QSR) | Generalized entropy index | Poverty Measures | At-risk-of-poverty threshold | At-risk-of-poverty rate | FGT poverty indices | Full Pipeline: Steps + Convey | Quality assessment | Publication table | Provenance | References

Introduction | Creating a RotativePanelSurvey | Accessing panel components | Applying steps to panel components | Estimation on panel components | Cross-sectional analysis (Implantation) | Comparison across follow-ups | PoolSurvey: Combined estimation | Pooled estimation | Multiple groups | Extracting surveys from panels | Time patterns | Loading panel data from files | Bootstrap replicate weights | Best practices | Next steps

Introduction | Developmental Growth Test | Fielded Fleet and Life Data Analysis | Reliability Growth Forecast | Simulating Failures | Weibull Comparison | Monte Carlo Analysis | Run the Monte Carlo Loop | Visualize the Parameter Distributions | Monte Carlo Summary | Sensitivity Analysis | Effect of Fleet Assessment Point | Effect of Growth Parameter | Limitations | Conclusion

Introduction | Installation | Before using | Main Features | What it doesn't support | Usage: Three Levels of Interaction | Level 1: Code Generation for torch::nn_module | Level 2: Direct Training Interface | Level 3: Conventional tidymodels Integration | Hyperparameter Tuning & Resampling | Variable Importance | Integration with | Learn More

What is rcrisp? | Why is consistent delineation important? | What does it do? | Workflow | Data considerations | Example | Interpretation and next steps | References

Run time by case and step | Key findings

Main Workflow | Credentials | Variable Dictionary | Findings | Labelled Survey Data | Survey Block Views

Event study vs. effect matrix | A toy non-absorbing panel | One-step fit: nabs_effect_cells() | Comparing methods | Working from existing objects | Collapsing back to an event study | The cell schema | Why not PanelMatch?

1. Why use R for this at all? | 2. Getting your data in: nabs_read_dta() | 3. Rosetta stone: did_multiplegt_dyn → nabs_event_study() | What about csdid / did_imputation / xtevent? | 4. Stata-style argument aliases | 5. Getting results out: nabs_write_dta() | Reproducibility checklist

The 30-second version | A simulated panel | Three estimators, three lines of code | Or call the estimators directly | Naive TWFE reference | Plot | Plot styles | Schema

Example 1: Research Trends Over Time | Collecting Data | Yearly Counts with Rolling Average | Master's vs PhD Trends | Example 2: Comparing Universities | Collecting University-Level Data | Top Universities Bar Chart | University Trends Over Time | PhD-to-Total Ratio | Example 3: Keyword and Abstract Analysis | Collecting Detailed Metadata | Keyword Frequency | Tips for Large-Scale Analysis | Saving Results Locally | Incremental Detail Retrieval | Rate Limiting

Responsible Use | More Than Downloading | Basic Search | Targeting Specific Fields | Filtering by Thesis Type and Access Status | The 2000-Result Limit | Advanced Search | Year and Language Filters | Group and Thesis Status | Combining Filters | Auto-Pagination | Detailed Search | Finding Valid Filter Values | Filtering by Institution | Filtering by Subject and Discipline | Filtering by Supervisor | Vector-Valued Parameters | Retrieving Detailed Metadata | Single Thesis | Batch Retrieval | Aggregate Statistics | Cache Management | Viewing Cache Status | Clearing Cache | Configuring Cache TTL | Request Configuration | Working with Results | Limitations and Best Practices | Technical Limitations | Best Practices | Citation

Why Real-Time Evaluation Matters | Setup | A Single-Quarter Illustration | Full Real-Time Loop | Method × Vintage Scoreboard | Forecasts vs. Realizations | Where to Go Next

Why Ragged Edges Matter | A Ragged-Edge Example | Comparing Indicator Completion Rules | Visualizing the completed indicator paths | Direct Alignment | Explicit Missing Values | When to Use Which Option

1. Input data | 2. Setting up the network | 3. Network cleaning | 4. Visualise cleaned network

References

Overview | Installation | Starting the MCP Server | Configuring Claude Code | Configuring Claude Desktop | Available Tools | Example Session | Security Considerations

Overview | Test Time | Solving for Test Time | Solving for Sample Size | Effect of Weibull Shape | Allowing Failures | Summary

Introduction | Mean Cumulative Function | Example | Using Data Frames | Censored Observations | Exposure-Adjusted MCF | Using Exposure Results with MCF | Exposure Analysis | Multi-Panel Plot | Single Panel Plots | Exposure with Censoring | Power Law NHPP | Log-Linear NHPP | Segmented NHPP | With User-Specified Breakpoints | Forecasting | Summary

What "absent" means | How seasight uses the signal | Minimal inspection pattern

Introduction | Setup | Loading the Workflow (Declarative YAML) | Instantiating the DAG | Visualizing the Workflow | Conclusion

Persistence Concepts | Declarative Pipeline | Running the Scenario | 1. Initial Run (The Pause) | 2. The Restart (The Success) | Summary

Overview | 🏗️ Step 1: Initialize an Isolated Repository | 🧠 Step 2: Declarative Workflow Loading | 🧘 Summary

1. The AgentDriver Base Class | 2. Testing with a Mock Driver | 3. Integration with YAML Workflows | Step 1: Define Your Workflow (extending_hydrar.yml) | Step 2: Use a Node Factory to Resolve the Driver | 4. Why Use Custom Drivers?

The "Agent-as-Target" Pattern | Example: Literature Synthesis Pipeline | Benefits of Integration | 1. Caching Expensive LLM Calls | 2. Dependency Tracking | 3. Resilience and Resumption | Conclusion

Rationale | Function to use | Basic Usage | Using Custom Activations in a Model | Skipping the Dry-Run Probe | Naming Your Custom Activation | Error Handling | Summary

What's so special about | Setup | Linear Regression as a Special Case | Data | Fitting the model | Evaluation | Comparison with lm() | Logistic Regression as a Special Case | Binary Logistic Regression | Comparison with glm() / nnet::multinom()

Overview | Setting the Stage | Creating an Experiment | Running the Simulation | Checking the Results | Analyzing the Data (Bonus Section) | Visualizing the NetLogo World (Bonus Section) | Wrapping up

Cosinor Analysis | Single-phase Model (i.e., single phase circadian rhythm) | Traditional Cosinor Model fitted via Ordinary Least Sqaure (OLS) | Note | Econometry-modified Cosinor Model with Feasible General Least Square (FGLS) | Dual-phase Model (i.e., 12 + 24hour rhythms) | Ecnometry-modified Cosinor Model with Feasible General Least Sqaure (FGLS) | Gaussian Kernel Density Estimation on Circularized Time | Visualized Model Comparisons | OLS-Cosinor vs. Kernel Density Estimation

What will be covered in this tutorial? | Step 0: Pre-requisits | Step 1: Segment Daily Recordings | Visual Example of Segmented Daily Recording | Step 2: Export Daily Recordings | Step 3: Generate Daily Reports

What will be covered in this tutorial? | Load the Libraries | What is Actigraphy? | Understanding Accelerometers and Gyroscopes | Accelerometers | Gyroscopes | On-Device Filtering and Data Acquisition | Signal Processing | Time Stamping and Time Zone Considerations | The Need for ActiGlobe | Getting Started with ActiGlobe | References

What will be covered in this tutorial? | Load the Libraries | Load Example Data: FlyEast | Step 0. Create a brief summary of the recordings | Step 1: Look into a travel diary | Step 2: Adjust time change based on the travel diary

Similar packages | Package Comparison | Complementary Use

Overview | Setting the Stage | Running the Simulation | Preparing the Data | Building the Plot | Creating an Animation | Wrapping up

Function Overview | 1. calc_emissions_enteric() Parameters | Required Parameters | Animal Characteristics | Production System | Feed Parameters | Methodology Control | 2. calc_emissions_manure() Parameters | Manure System | Tier 2 Specific | Nitrogen Management | 3. calc_emissions_soil() Parameters | Nitrogen Inputs | Site Conditions | Emission Factors | 4. calc_emissions_energy() Parameters | Fuel Consumption | Location and Factors | Grid Emission Factors (Built-in) | 5. calc_emissions_inputs() Parameters | Feed Inputs | Other Inputs | Regional Factors | Advanced Options | 6. calc_intensity_litre() Parameters | Milk Composition | FPCM Calculation Formula | 7. calc_intensity_area() Parameters | Area Breakdown | Valid area_breakdown Names | Validation | 8. calc_batch() Parameters | Data Input | Template Column Requirements | Processing Options | 9. Parameter Validation and Quality Control | Automatic Validations | Data Quality Indicators | 10. Common Parameter Issues and Solutions | Missing Data Handling | Unit Conversion Guide | Regional Parameter Adjustments | 11. Parameter Sensitivity Rankings | High Impact Parameters (>15% result change) | Medium Impact Parameters (5-15% result change) | Low Impact Parameters (<5% result change) | 12. Troubleshooting Common Issues | Error Messages and Solutions | Performance Optimization | 13. Advanced Parameter Combinations | Tier 2 Optimal Parameter Sets | Parameter Interaction Effects | 14. Data Collection Protocols | Minimum Data Requirements by Objective | Data Collection Schedule | 15. Quality Assurance Framework | Validation Hierarchy | Red Flag Indicators | Conclusion

What does cowfootR do? | Installation and loading the package | Step 1: Define system boundaries | Units and reporting basis | Step 2: Calculate emissions by source | Enteric fermentation | Manure management | Soil emissions | Energy use | Purchased inputs | Step 3: Aggregate total emissions | Step 4: Calculate intensity metrics | Milk intensity | Area intensity | Interpreting results | What’s next? | Summary

Overview | How to Use This Vignette | Theoretical Background | Life Cycle Assessment in Dairy Production | Key Emission Sources in Dairy Systems | System Boundaries | Functional Units and Intensity Metrics | Getting Started with cowfootR | Installation | Loading the Package | Input Data Structure | Basic Workflow | Data Requirements: Required vs Optional | Required Data | Optional but Recommended Data | Example: Basic Farm Assessment | Step 1: Define System Boundaries | Step 2: Basic Farm Data | Step 3: Calculate Emissions by Source | Enteric Fermentation | Manure Management | Soil Emissions | Energy Use | Purchased Inputs | Step 4: Aggregate Total Emissions | Step 5: Calculate Intensity Metrics | Milk Intensity | Area Intensity | Visualizing Results | Emission Source Breakdown | Intensity Comparison | Understanding the Results | Interpreting Emission Factors | Benchmarking Performance | Common Issues | Next Steps | Key Takeaways

Understanding IPCC Methodology Tiers | Data Requirements Analysis | Tier 1 Data Needs | Tier 2 Additional Requirements | Input columns used by calc_batch() for Tier 2 calculations | Core required columns (all tiers) | Tier 2–relevant optional columns by emission source | Enteric fermentation (Tier 2 refinement) | Young stock (optional refinement) | Manure management (Tier 2 refinement) | Other emission sources | Complete column specification | Theoretical Background | IPCC Tier System Overview | Key Differences in Dairy Applications | Methodological Differences by Emission Source | Enteric Fermentation | Tier 1 Approach | Tier 2 Approach | Manure Management | Tier 1 vs Tier 2 Comparison | Comprehensive Farm Comparison | Farm Profile | Tier 1 Assessment | Tier 2 Assessment | Results Comparison | Visualization of Differences | Impact on Intensity Metrics | Accuracy and Uncertainty | Uncertainty Ranges by Tier | Factors Affecting Accuracy | Decision Framework: When to Use Each Tier | Tier Selection Criteria | Cost-Benefit Analysis | Sensitivity Analysis | Parameter Sensitivity in Tier 2 | Impact on Farm Rankings | Practical Recommendations | Implementation Strategy | For Research and Policy | For Farm Advisors | For Farmers | Quality Assurance | Conclusions | Key Findings | Selection Guidelines

Comprehensive Single Farm Carbon Footprint Analysis | Farm Profile: Estancia Las Flores | Step 1: Define system boundaries | Step 2: Provide farm data (inputs) | Herd Composition and production | Feed and nutrition | Land use and soil management | Energy consumption | Additional purchased inputs | Step 3: Calculate emissions by source | Enteric fermentation | Key arguments. | Manure management | Soil N2O Emissions | Energy-Related Emissions | Purchased Input Emissions | Step 4: Total emissions and hotspot analysis | Visualize emission sources (bar chart) | Step 5: Intensity calculations | Milk Intensity (kg CO₂eq per kg FPCM) | Area Intensity (kg CO₂eq per ha) | Benchmarking (interpretation note) | Step 6: Scenario analysis (example) | Scenario 2: Enhanced Manure Management | Step 7: Two complementary visual summaries | Step 8: Summary table (KPI-style) | Conclusion | For multi-farm processing, see the Batch Farm Assessment vignette.

Single template-statement pair | Processing multiple statements | Advanced templating | Optional information in templates | Attribution and sources | Validation | Defining a schema | Validating data | Under construction - much more to come!

The fcmconfr Workflow | 1. Import FCMs | 1.1 Importing Conventional FCMs | 1.2 Importing IVFN FCMs | 1.3 Importing TFN FCMs | 1.4 Interacting with IVFN and TFN Adjacency Matrix Elements | 1.5 Viewing Imported FCMs | 2. Set Simulation Parameters using fcmconfr_gui() | Optional: Selecting an Appropriate Lambda | 3. Run fcmconfr() | 4. Explore fcmconfr() Results | 4.1 Visualizing fcmconfr() Results | 4.2 Retrieving Inferences with get_fcmconfr_inferences() | 4.3 Retrieving Inferences with summary() | Advanced Inference Options

Introduction | The Crow-AMSAA Model | Example | The Piecewise NHPP Model | The Piecewise NHPP with Change Point Detection | The Duane Model | Goodness of Fit | Data Conversion | Summary

Introduction | Importing Data | Importing from CSV Files | Importing from Excel Files | Arranging Data | Censored Data | Missing, NAs, NANs and Infs | Removing Missing Values | Replacing Missing Values | Exporting Data | Exporting to CSV Files | Exporting to Excel Files

Workflow overview | What the diagram covers | Reproducibility note

Loading FCMs into R for fcmconfr | Types of FCMs | 1. Conventional FCMs | 1.1 - Conventional FCMs from Different Sheets in a Single .xlsx File | 2. IVFN FCMs | 2.1 - Loading an IVFN-FCM from a single .xlsx file: | 2.2 - Loading an IVFN-FCM from Separate Files in the Same Directory | 2.3 - Loading Multiple IVFN-FCMs from Separate Files in the Same Directory | 2.4 - Loading Multiple IVFN-FCMs from Separate Files in Different Directories | 3. TFN FCMs | 3.1 - Loading a TFN-FCM from a Single .xlsx File | 3.2 - Loading a TFN-FCM from Separate Files in the Same Directory | 3.2 - TFN FCMs from a Directory of Adj. Matrices in Single .xlsx Files | 3.3 - TFN FCMs from a Adj. Matrices in Separate .xlsx Files Stored in Different Directories