Downloading Climate Normals | Downloading current normals (1991-2020) | Downloading previous normals (1981-2010 and 1971-2000) | Finding stations with specific measurements | Understanding Climate Normals

General descriptions | Original names and units | 1991-2020 | 1981-2010 and 1971-2000

What are flags/codes | Flags - Weather Data | Codes | Codes - Climate Normals

Stations | Weather | Climate Normals

Packages | General usage | Data gaps | Multiple weather columns

Summary | Statement of need | A note on performance | Example of use | Additional materials | Acknowledgements | References

tidyterra | Why tidyterra? | A note for advanced terra users | Get started with tidyterra | SpatRaster objects | SpatVector objects | Plotting with ggplot2

Brushes | Good brushes | Hands | Rough lines and polygons | Pattern Fills | Rough drawing and mypaint_device | ggplot2 | Using mypaintr in knitr | Advanced hand control: pressure, speed and tilt

Pressure on the default device | Pressure using mypaint_device() and brushes

Reproducing | Semantics | Build Cost | Detect | Count | Extract | Summary

Ichimoku Kinko Hyo | Definition | Interpretation | Context | A Typical Workflow | Data | Input data requirements | ichimoku() | ichimoku.ichimoku() | Preserving Data | Defining custom holidays | Working with ichimoku objects | plot() | 1. Static Plots | Oscillators | Custom Sub-plots | Further Options | 2. Interactive Plots | Implementation notes | archive() | Writing | Reading | Data Integrity Verification | Supplementary Information | Data validation | ichimoku object specification | API modifications | Since version 0.x | Dependencies | References

Introduction | System Requirements | Creating MLX Arrays | Lazy Evaluation | Arithmetic Operations | Matrix Operations | Matrix Multiplication | Transpose | Cross Products | Reductions | Indexing | Device Management | Performance Comparison

1. Introduction | mirai | mirai (advanced) | daemons | 2. Error Handling | 3. Local Daemons | With Dispatcher (default) | Without Dispatcher | everywhere() | 4. Memory Management | Queue Backpressure | Non-blocking Submission | Shared Memory with Local Daemons | 5. mirai_map | Basic Usage | Collecting Options | Multiple Map | Nested Maps | 6. Remote Infrastructure | Remote Daemons Overview | Launching Remote Daemons | SSH Direct Connection | SSH Tunnelling | HPC Cluster Resource Managers | Job Arrays | HTTP Launcher | Default: Posit Workbench | Custom HTTP APIs | Troubleshooting | Generic Remote Configuration | Manual Deployment | TLS Secure Connections | Automatic Zero-configuration Default | CA Signed Certificates | 7. Compute Profiles | with_daemons() and local_daemons() | With Method | 8. Advanced Topics | Random Number Generation | Synchronous Mode

Overview | Problem Setup | Define the Loss Function | Automatic Differentiation | Training Loop with SGD | Method 2: Closed-form Regression via Matrix Algebra | Accelerating the Closed-form Solution with mlx_compile() | Accuracy and Performance Comparison | Device Selection

Building a Neural Network | Generating Training Data | Training Loop | Evaluating Model Performance | Using Different Architectures | Convolutional Features | Embeddings for Categorical Data | Available Loss Functions | Available Activation Functions | Regularization and Normalization | Best Practices | Further Reading

1. Migration from future_promise() | 2. Setting the random seed | 3. Accessing package functions during development | 4. Why does mirai() take time when it's meant to return immediately? | 5. Creating daemons on-demand or shutting down idle daemons | 6. Launching daemons --vanilla

Core Concepts | Key Takeaways | 1. Basic mirai Usage | Create and Access Results | Passing Data | 2. Local Daemons | Basic Setup | Daemon Configuration | Synchronous Mode (Testing/Debugging) | 3. Remote Daemons - SSH Direct | Setup Host to Accept Remote Connections | URL Constructors | SSH Configuration | 4. Remote Daemons - SSH Tunnelling | When to Use Tunnelling | Setup | 5. HPC Cluster Configurations | General Pattern | Scheduler-Specific Directives | 6. HTTP Launcher | 7. Manual Daemon Deployment | Generate Launch Commands | 8. Compute Profiles | Multiple Independent Profiles | Scoped Profiles | 9. Common Patterns | Temporary Daemons | Mixed Local/Remote Resources | Dynamic Scaling | 10. mirai_map - Parallel Map | Basic Usage | Collection Options | Multiple Map (over DataFrame/Matrix) | 11. Error Handling | 12. Monitoring | 13. Advanced Features | Timeouts | Cancellation | Evaluation Everywhere | Random Seeds (Reproducible) | Custom Serialization | TLS Configuration | 14. Dispatcher vs. Direct | 15. Quick Decision Tree | 16. Common Gotchas

Introduction | Basic usage | Chopping by width and number of elements | Even more ways to chop | Isolating common values | Quick tables | Specifying labels | Specifying breaks | Chopping dates, times and other vectors

Why this name? | Example | Multilingual! | Exercises | Recommended resources about Git

How it works | Parallel functional programming | Asynchronous parallel functional programming | Auto-scaling | Caveats and limitations

Logging worker processes | In targets

Tasks vs workers | How to use crew | Synchronous functional programming | Asynchronous functional programming | Summaries | Termination | Monitoring local processes | Tuning and auto-scaling | Crashes and retries

Resource usage | Workers | Crashes | Ports | Security | Perimeters | Encryption | Certificate authorities

About | How it works | Scope | Implementation | Network | Example | Batched launches | Controllers | Informal testing | Load testing | Managing workers

Agent Skill | 1. Developer Interfaces | 2. Guidance

Instalación | Uso de igraph | Crear un grafo | IDs de vértices y aristas | Añadir y borrar vértices y aristas | Construcción de grafos | Establecer y recuperar atributos | Propiedades estructurales de los grafos | Búsqueda de vértices y aristas basada en atributos | Selección de vértices | Selección de aristas | Tratar un grafo como una matriz de adyacencia | Diseños y graficación | Algoritmos de diseño | Dibujar un grafo utilizando un diseño | Atributos de los vértices para graficar | Atributos de las aristas para graficar | Argumentos más comunes de plot() | igraph y el mundo exterior | Dónde ir a continuación | Información de la sesión

Installation | Usage | Creating a graph | Vertex and edge IDs | Adding/deleting vertices and edges | Constructing graphs | Setting and retrieving attributes | Structural properties of graphs | Querying vertices and edges based on attributes | Selecting vertices | Selecting edges | Treating a graph as an adjacency matrix | Layouts and plotting | Layout algorithms | Drawing a graph using a layout | Vertex attributes controlling graph plots | Edge attributes controlling graph plots | Generic arguments of plot() | igraph and the outside world | Where to go next | Session info

1. HTTP Client | ncurl: Basic Requests | ncurl_aio: Async Requests | Promises Integration | ncurl_session: Persistent Connections | 2. WebSocket Client | 3. Unified HTTP/WebSocket Server | Handler Types | HTTP Request Handlers | Static Content Handlers | WebSocket Handlers | HTTP Streaming Handlers | Server-Sent Events | 4. Secure Connections (TLS) | Public Internet HTTPS | Self-Signed Certificates | 5. Client Example: Shiny ExtendedTask | 6. Server Example: Quarto Site with Dynamic API

What is Polars? | Documentation and tutorials | Series and DataFrames | Methods and pipelines | Subset | Aggregate and modify | Reshape | Join | Lazy execution | Data import | Execute R functions within a Polars query | Data types

Writing functions using polars expressions | Using purrr | Conclusion

Why ggtypst? | Installation | First plot | When to use annotate, geom, and element | R raw strings | Text, Typst math, and MiTeX math | Typst markup content | Native Typst math | MiTeX-backed LaTeX math | annotate_*(): one-off plot annotations | geom_*(): data-driven labels | element_*(): Typst in theme elements | Next steps

Test Environment | Layer 1: Typst Rendering | Results | Analysis | Layer 2: Full Plot Performance | 2A: API Type Comparison | 2B: Content Complexity | 2C: Geom Row Scaling | Practical Guidelines

What it is | What it contains | How to import it

How to install | From R-multiverse (recommended) | From GitHub | Details of installation | Pre-built Rust library binaries | Rust build time options | Features | Profile | Minimum Supported Rust Version (MSRV) | Builds for WASM/Emscripten

Keyboard shortcuts

Overview | Get state | From R | From HTTP | From WebSockets | Get Renderers | Render plot | Remove plots | Get static IDs | Security

System requirements

1. Event-driven promises | 2. Shiny ExtendedTask: Introduction | 3. Shiny ExtendedTask: Cancellation | 4. Shiny ExtendedTask: Generative Art | 5. Shiny ExtendedTask: mirai map | 6. Shiny Async: Coin Flips | 7. Shiny Async: Progress Bar | 8. Plumber GET Endpoint | 9. Plumber POST Endpoint

Basic usage | Build the image | Run the container | Start the device | Advanced usage | Set defaults in Rprofile

Usage | Troubleshooting

Test Plots | Methodology | Environment | SVG | PNG | PDF | TIFF | File Sizes | Summary

Architecture overview | Package setup | DESCRIPTION | Include the API header | Initialization | Types | Handle types | ID types | Structs | unigd_graphics_client | unigd_device_state | unigd_render_args | unigd_render_access | unigd_find_results | unigd_renderer_info | Attaching to a device | Querying device state | Browsing plot history | Rendering plots | Retrieving a client from a device | Logging | Memory management | Thread safety | API reference | General | Client registration | Device operations | Plot history | Rendering | Renderers

Plot rendering in base R | Plot rendering with unigd | In-memory render access | More features | Zoom | Paging (by index) | Plot IDs | Special renderers | Performance considerations

How does tidypolars translate R expressions into Polars expressions? | Single values, column names, and external objects | Functions | Built-in functions | User-defined functions | Special case: across() | Non-translated functions | A note on performance

Core Concepts | Key Takeaways | 1. Sockets and Connections | Create Sockets | Protocols | Transports | 2. Send and Receive | Synchronous | Receive Modes | 3. Async I/O | Basic Async | Non-blocking Patterns | 4. Condition Variables | Basics | Pipe Notifications | Async with CV | 5. Request/Reply (RPC) | Server | Client | 6. Pub/Sub | 7. Surveyor/Respondent | 8. TLS Secure Connections | Self-signed Certificates | CA Certificates | 9. Options and Statistics | Get/Set Options | Common Options | Custom Serialization | Statistics | 10. Contexts | 11. Cross-language Exchange | R to Python (NumPy) | 12. Error Handling | 13. Utilities

1. TLS Secure Connections | 2. Options | 3. Custom Serialization | 4. Statistics

1. Cross-language Exchange | 2. Async and Concurrency | 3. Synchronisation Primitives

1. Request Reply Protocol | 2. Publisher Subscriber Protocol | 3. Surveyor Respondent Protocol

Basics | Percent class | format() method | Casting and coercion | Double dispatch | Percent class | Decimal class | Cached sum class | Record-style objects | Rational class | Decimal2 class | Equality and comparison | Polynomial class | Make an atomic polynomial vector | Implementing equality and comparison | Arithmetic | Cached sum class | Meter class | Implementing a vctrs S3 class in a package | Getting started | Low-level and user-friendly constructors | Other helpers | Testing | Existing classes

Introduction | Example: Eradication of Roaches using holdout validation approach | Coding the Stan model | Setup | Holdout validation | Splitting the data between train and test | Fitting the model with RStan | Computing holdout elpd: | K-fold cross validation | Splitting the data in folds | Fitting and extracting the log pointwise predictive densities for each fold | Computing K-fold elpd: | References

Introduction | Example: Eradication of Roaches | Coding the Stan model | Setup | Fitting the model with RStan | Moment matching correction for importance sampling | Using loo_moment_match() directly | References

Introduction | Setup | Example: Poisson vs negative binomial for the roaches dataset | Background and model fitting | Roaches data | Fit Poisson model | Using the loo package for model checking and comparison | Computing PSIS-LOO and checking diagnostics | Plotting Pareto $k$ diagnostics | Marginal posterior predictive checks | Try alternative model with more flexibility | Comparing the models on expected log predictive density | References

Introduction | Data | Reference model | Variable selection | Preliminary cv_varsel() run | Final cv_varsel() run | Predictive performance plot from final cv_varsel() run | Decision for final submodel size | Predictive performance table from final cv_varsel() run | Predictor ranking(s) from final cv_varsel() run and identification of the selected submodel | Post-selection inference | Marginals of the projected posterior | Predictions | Supported types of models | Troubleshooting | Non-convergence of predictive performance | Overfitting | Issues with the traditional projection | Issues with the augmented-data projection | Speed | References

Setting up doctest | Converting @examples to @doctest sections | Before | After | Creating doctests by adding expectations | Doctests for messaging functions | Adding doctests for utility functions | Adding doctest to Suggests: | Conclusion

Introduction | General idea | Implementation | Example: Poisson distribution | Data | Reference model | Variable selection using the latent projection | Variable selection using the traditional projection | Conclusion | Example: Negative binomial distribution | Censored observations (survival analysis) | Example: Weibull distribution with right-censored observations | Example: Log-normal distribution with right-censored observations | References

1. Mirai Parallel Clusters | 2. Foreach Support

1. Introduction | 2. Automatic Tracing Setup | 3. Span Types and Hierarchy | 3.1 Core Span Types | 3.2 Span Relationships and Context Propagation | 4. Status and Error Tracking | 5. Monitoring and Observability | 6. Integration with Observability Platforms

1. Serialization: Arrow, polars and beyond | 2. Serialization: Torch | 3. Database Hosting using Arrow Database Connectivity | 4. Shiny / mirai / DBI / ADBC Integrated Example

Timescales and phylogenies | Phylogenies with only fossil taxa | Circular phylogenies | Circular phylogenies with "stacked" timescales | Disclaimer | Axis timescales on radial phylogenies | Tip labels on radial phylogenies

coord_trans meets coord_flip | 2D linear transformations

Introduction | About this document | Huxtable | Installation | Getting started | Huxtable properties | Selecting rows and columns | Editing huxtables | Formatting cell text | Aligning text and numbers | Borders | Changing and adding content | Changing the layout | Table position and column width | Headers | Splitting tables | Themes | Conditional formatting | Output to different formats | Pretty-printing data frames | Using huxtables in knitr and rmarkdown | Quick output commands | Useful output options | Creating a regression table | Getting more information

Introduction | Graphical posterior predictive checks (PPCs) | Setup | Example models | Defining y and yrep | Histograms and density estimates | ppc_dens_overlay | ppc_hist | Distributions of test statistics | ppc_stat | Other PPCs and PPCs by group | ppc_stat_grouped | Providing an interface to bayesplot PPCs from another package | Defining a pp_check method | Examples of pp_check methods in other packages | References

Introduction | Setup | Example model | Posterior uncertainty intervals | mcmc_intervals, mcmc_areas | Univariate marginal posterior distributions | mcmc_hist | mcmc_hist_by_chain | mcmc_dens | mcmc_dens_overlay | mcmc_violin | Bivariate plots | mcmc_scatter | mcmc_hex | mcmc_pairs | Trace plots | mcmc_trace | mcmc_trace_highlight | References

Abstract | Introduction | Continuous Data | Count Data | Benefits of Bayesian Methods | Conclusion | Acknowlegements | References | Appendix A: Refresher on p-values | Appendix B: Hierarchical Example

Introduction | Repeated Binary Trials | Baseball Hits (Efron and Morris 1975) | Pooling | Fitting the Models | Complete Pooling | No Pooling | Partial Pooling | Observed vs. Estimated Chance of Success | Posterior Predictive Distribution | Evaluating Held-Out Data Predictions | Simulating Replicated Data | Prediction for New Trials | Calibration | Sharpness | Why Evaluate with the Predictive Posterior? | $\log E[p(\tilde{y} , | , \theta)]$ vs $E[\log p(\tilde{y} , | , \theta)]$ | Posterior expectation of the log predictive density | Approximating the expected log predictive density | Predicting New Observations | Estimating Event Probabilities | Multiple Comparisons | Ranking | Who has the Highest Chance of Success? | Graphical Posterior Predictive Checks | Test Statistics and Bayesian $p$-Values | Comparing Observed and Replicated Data | Discussion | Exercises | References | Additional Data Sets | Rat tumors (N = 71) | Surgical mortality (N = 12) | Baseball hits 1996 AL (N = 308)

Introduction | Step 1: Specify a posterior distribution | Note on "prior beliefs" and default priors | Step 2: Draw from the posterior distribution | Step 3: Criticize the model | Step 4: Analyze manipulations of predictors | Troubleshooting | Markov chains did not converge | Divergent transitions | Maximum treedepth exceeded | Conclusion | References

Introduction | Likelihood | Priors | Posterior | Logistic Regression Example | Conditional Logit Models | Binomial Models | Going Further | References

Introduction | Likelihood | Priors | Posterior | Linear Regression Example | Model comparison | The posterior predictive distribution | Graphical posterior predictive checks | Generating predictions | Gamma Regression Example | References

Introduction | Likelihood | Priors | Posterior | Poisson and Negative Binomial Regression Example | References

What rredlist is not: | Installation | Authentication | Overview of available features | Example usage | Loading the package | Search for assessments for a particular species | Search for assessments that recommend particular conservation actions | Get a list of all conservation actions | Return assessments with a particular conservation action | Advanced usage | High level vs. low level interfaces | High level interface | Low level interface | Usage best practice | Citing the IUCN Red List API | Rate Limiting | API Versioning

Introduction | Head-to-head benchmarks | 1. Get species count | 2. Lookup individual assessment | 3. Taxonomic lookup with defaults | 4. Taxonomic lookup with query (one page of results) | 5. Taxonomic lookup with query (~10 pages of results) | 6. Taxonomic lookup with query (~40 pages of results) | 7. Taxonomic lookup with query (~900 pages of results) | And the winner is... | Query breakdown | Conclusion

Introduction | How have mollusc assessments increased through time? | What is the conservation status of Australian reptiles? | What habitats do conifers and cycads occur in? | Some recent published uses of rredlist

Stratigraphic columns | Plot stratigraphic column | Use stratigraphic patterns | Further customization

Classes and objects | Generics and methods | Method dispatch and inheritance

Do not include as_polars_df() or as_polars_lf() in the timing | Use lazy execution when possible | Use streaming mode when possible | Do not try to run tidypolars in parallel

Regression tables with huxreg | Altering data

Lazy vs eager execution | Order of operations | How does polars help? | Use the streaming engine | Use polars functions

What is Polars? | Documentation and tutorials | Series and DataFrames | Methods and pipelines | Subset | Aggregate and modify | Reshape | Join | Lazy execution | Data import | Execute R functions within a Polars query | Data types

How to install | From R-universe (recommended) | Details of installation | Pre-built Rust library binaries | Rust build time options | Features | Profile | Minimum Supported Rust Version (MSRV)

Lazy vs eager execution | Order of operations | How does polars help? | Use polars functions | Streaming data

Introduction | Getting started | Polars quick exploration guide | Polars expressions | Expressions | Contexts | GroupBy | Folds | Window functions | List context and row wise computations | Custom functions | R examples

Converting data between Polars and R | From R to Polars | From Polars to R | 64-bit integers | The Object data type

Plot occurrences through time | Geological timescale color scales for ggplot | Facetting with the geological timescale

Introduction | Core Auxiliary Functions | tradingDays() | look() | Dataframe Constructors | xts_df() | matrix_df() | Dataframe Utilities | df_merge() | df_append() | Timestamp Utilities | format_POSIXct()

Introduction | Working with Strategies | 1. Use ichimoku() | 2a. Simple strategies using strat() | 2b. Complex strategies using strat() | 3. View strategy summary with summary() | 4. Visualize by calling plot() or iplot(). | 5. Combine strategies using stratcombine() | 6. Further backtesting and returns analysis | Advanced Strategies and ML | autostrat() | autostrat() Levels 2 and 3 | mlgrid() | relative() | Strategy Summary Specification | References

About OANDA fxTrade | Registering with OANDA | Setting your OANDA API Key | Using a Live Account | The OANDA Functions | oanda() | oanda_chart() | oanda_studio() | Downloading Data | oanda_stream() | oanda_quote() | oanda_view() | oanda_orders() / oanda_positions() | Available Instruments | Caching Implementation | References

Adding the Data | Adding the Model | Adding the Posterior | Checking the final posterior, data and model | Add Posterior Reference Draws

How to run targets examples from vignettes | tar_terra_rast(): targets with terra rasters | Raster metadata | tar_terra_vect(): targets with terra vectors | tar_terra_sprc(): targets with terra raster collections | tar_terra_sds(): targets with terra raster datasets

EpiGantt examples | Start with the Line List | Transform the Line List into long format for ggplot | Plot the Epigantt chart | Outbreak 2: Fictional Varicella Outbreak in Berlin

The seasonal plot | Seasonal alignment and plot | Combining everything for the seasonal plot | Other visualisations

Introduction | Setup | Example model | Diagnostics for the No-U-Turn Sampler | Divergent transitions | mcmc_parcoord | mcmc_pairs | mcmc_scatter | mcmc_trace | mcmc_nuts_divergence | Energy and Bayesian fraction of missing information | mcmc_nuts_energy | General MCMC diagnostics | Rhat: potential scale reduction statistic | mcmc_rhat, mcmc_rhat_hist | Effective sample size | mcmc_neff, mcmc_neff_hist | Autocorrelation | mcmc_acf, mcmc_acf_bar | References

How to use use_java() | How to use with targets and callr

About | Example: coin flips, no promises | Tutorial | Full app code | Example: coin flips, with promises

Eager and lazy evaluation | Importing data | Example

What is an extent? | Helper functions to create multiple extents of a raster | tile_n() | tile_grid() | tile_blocksize() | How to run targets examples from vignettes | Example targets pipeline

Epi Curve examples | Ebola Outbreak in Kikwit, Democratic Republic of the Congo 1995 | SARS Outbreak in Canada 2003 | Influenza Data from Germany 2020-2025 | Extra

Backup controllers

Get started | Create slides | Example (---) | Example (##) | Keyboard shortcuts | CSS and styling | Example: section numbers | Example: TOC | Responsive layout | Printing | Slide attributes | Built-in classes | Example: an inverse slide | Example: center content | Example: fade a slide | Example: a background image | Example: an editable slide | Miscellaneous elements | Page numbers | Timer | Caveats | Lengthy slides | Page mode | Aspect ratio | Zooming | Cross-browser/device issues | Technical notes | The original HTML | snap.js | snap.css | Enjoy!

Method registration | Documentation and exports | Backward compatibility

Validation | Basics | When is validation performed? | Avoiding validation | Properties | Default value | Computed properties | Dynamic properties | Common Patterns | Deprecated properties | Required properties | Frozen properties | Constructors

S3 | Methods | Classes | List classes | S4 | Unions

Generic-method compatibility | Generic with dots; method without dots | Generic and method with dots | Generic and method without dots | Customizing generics | Add required arguments | Add optional arguments | Do some work | super() | Multiple dispatch | A simple example | Special "classes"

Getting Data | Aggregating data by regions | Plotting the data

Introduction | Deploying an R package on release | Hosting the resulting package | Creating a WebAssembly CRAN-like repository | The GitHub Actions build process

Compiling R packages for WebAssembly | Setting up the WebAssembly toolchain | Using the webR Docker container | WebR development installation | Installing the rwasm package | Building an R package | Adding an R package to a package repository | Managing and using the repository | Local testing | Deployment to static hosting

Create filesystem images | Emscripten's file_packager tool | Compression | Mount .tar archives as a filesystem image | Building an R package library image | Local testing

Introduction | Building an R package library | Mounting host directories

Filesystem metadata | Archive data layout

1. Quickly Download and Install Java | Install rJavaEnv | Quick Install Java JDK | 2. Check Java installation | Note for Linux users | 3. Return things to their original state | Unset Java | Delete Java from the project directory | 4. Complete Cleanup

1. Set-up cache folder location | 2. Download Java JDK distributions | 3. Manage downloaded Java JDK distributions | 4. Install from downloaded Java JDK distributions into current project | 5. Manually set installed Java environment | 6. Cleanup

Introduction | Installing CmdStan | Compiling a model | Running MCMC | Posterior summary statistics | Summaries from the posterior package | CmdStan's stansummary utility | Posterior draws | Extracting draws | Plotting draws | Sampler diagnostics | Extracting diagnostic values for each iteration and chain | Sampler diagnostic warnings and summaries | CmdStan's diagnose utility | Running optimization and variational inference | Optimization | Laplace Approximation | Variational (ADVI) | Variational (Pathfinder) | Saving fitted model objects | Comparison with RStan | Additional resources

Adding geological timescales | Scales on other axes | Add multiple timescales | Stack multiple scales | Timescales and faceted plots | Resize labels to fit inside interval rectangles | Scales on discrete axes | Custom discrete scales | More advanced topics

Introduction | Example | Simulated data | MCMC convergence diagnostics | Pareto-$\hat{k}$ | Pareto smoothing | Minimum sample size required | Convergence rate | Pareto-$\hat{k}$-threshold | Pareto diagnostics | Discussion | Reference

Introduction | Installation | Example | Draws formats | Available formats | Converting between formats | Converting regular R objects to draws formats | Example: create draws_matrix from a matrix | Example: create draws_matrix from multiple vectors | Manipulating draws objects | Subsetting | Mutating (transformations of variables) | Renaming | Binding | Summaries and diagnostics | summarise_draws() basic usage | Changing column names | Using custom functions | Specifying functions using lambda-like syntax | Other diagnostics | Other methods for working with draws objects | References

Summary statistics | Extracting posterior draws/samples | Structured draws similar to rstan::extract()

System requirements | macOS | Linux | Debian, Ubuntu, etc. | Fedora, CentOS, RHEL, etc. | Fedora, EPEL, etc.

Introduction | $M$-step-ahead predictions | Approximate $M$-SAP using importance-sampling | Autoregressive models | Case Study: Annual measurements of the level of Lake Huron | 1-step-ahead predictions leaving out all future values | Exact 1-step-ahead predictions | Approximate 1-step-ahead predictions | $M$-step-ahead predictions leaving out all future values | Exact $M$-step-ahead predictions | Approximate $M$-step-ahead predictions | Conclusion | References | Appendix | Appendix: Session information | Appendix: Licenses

Introduction | Setup | Example: Primate milk | Example: Oceanic tool complexity | Simpler coding using loo_model_weights function | References

Introduction | LOO-CV for multivariate normal models | Approximate LOO-CV using integrated importance-sampling | Exact LOO-CV with re-fitting | Lagged SAR models | Case Study: Neighborhood Crime in Columbus, Ohio | Fit lagged SAR model | Approximate LOO-CV | Exact LOO-CV | Working with Stan directly | Conclusion | References

Introduction | Setup: load packages and set seed | Model | Dataset | PSIS estimators and Pareto-$k$ diagnostics | Mixture estimators | Comparison with benchmark values obtained with long simulations | References

Introduction | Setup | Example: Well water in Bangladesh | Coding the Stan model | Fitting the model with RStan | Approximate LOO-CV using PSIS-LOO and subsampling | Adding additional subsamples | Specifying estimator and sampling method | Approximate LOO-CV using PSIS-LOO with posterior approximations | Combining the posterior approximation method with subsampling | Comparing models | References

Introduction | Example: Well water in Bangladesh | Coding the Stan model | Fitting the model with RStan | Computing approximate leave-one-out cross-validation using PSIS-LOO | Comparing models | References

Data frames of visualizations | Pre-generated images | R-generated visualizations | Customizing your Trelliscope app | Adding panels | Adding variables | Special variable types | Updating display attributes with pipe functions | Setting variable labels and tags | Setting panel options | Setting the default state of the app | set_default_labels() | set_default_layout() | set_default_sort() | set_default_filters() | Defining "views" | Specifying user inputs | Writing and viewing the app | Putting it all together

Sharing Trelliscope displays | Sharing the app directory | GitHub Pages | R Markdown and Shiny | Password Protection | Embedding Trelliscope in R Markdown | Embedding Trelliscope in Quarto | Embedding Trelliscope in Shiny

Dictionary-based compression | Why should I use a dictionary? | When is a dictionary useful? | How do I train a dictionary? | How large should my dictionary be? | How many samples should I provide to the dictionary builder? | How do I determine if a dictionary will be effective? | When should I retrain a dictionary? | Example

Arranging plots with deeptime | Combining plots | Other resources for arranging plots

Plot disparity through time | Disparity in base R | Phylomorphospaces

Work with DB | Work with R Data Frames

Output formats | Use with {DBI} connections | Without database connections | Engine options | Compiler options | Parameterized PRQL code blocks | Use query strings in R code blocks | Set special info string to output SQL code blocks

Introduction | Compilation | Immediate compilation | Delayed compilation | Pedantic check | Stan model variables | Executable location | Processing data | Named list of R objects | JSON file | R dump file | Writing CmdStan output to CSV | Default temporary files | Non-temporary files | Reading CmdStan output into R | Lazy CSV reading | read_cmdstan_csv() | as_cmdstan_fit() | Saving and accessing advanced algorithm info (latent dynamics) | Developing using CmdStanR | Pre-compiled Stan models in R packages | Troubleshooting and debugging

Download genome assemblies | Download metadata | Parse metadata files

Challenges with S3 | Challenges with S4

Introduction | The rvars datatype | rvar_factor and rvar_ordered subtypes | The draws_rvars datatype | Math with rvars | Expectations and summary functions | Constants | Using existing R functions and expressions with rvars | Converting functions with rfun() | Evaluating expressions with rdo() | Evaluating random number generators with rvar_rng() | Broadcasting | Slicing and conditionals | Subsetting rvars by draw: x[] | Conditionals using rvar_ifelse() | Selecting different elements in each draw: x[[]] | Applying functions over rvars | Looping over draws and rvars | Using rvars in data frames and in ggplot2

Prototype | Base prototypes | Coercing to common type | Casting to specified type | Size | Slicing | Common sizes: recycling rules | Appendix: recycling in base R

Using facet_panels() with ggplot2 | Custom R-generated panels with panel_lazy() | Panels of images existing on the web with panel_url() | Panels of images on the local filesystem with panel_local() | Setting panel options

Option 1: Using RStan for all chunks | Option 2: Using CmdStanR for all chunks | Example | Option 3: Using both RStan and CmdStanR in the same R Markdown document | Caching chunks | Running interactively

Motivation | Setup | Basic usage | Allowing the user to hide a message | Setting an expiry time | Using onetime in your package | Utility functions | Going further

Creating manhattan plots | Creating Q-Q plots

Introduction | Adding profiling statements to a Stan program | Accessing the profiling information from R | Comparing to a faster version of the model | Per-gradient timings, and memory usage | Accessing and saving the profile files | References

What is targets? | How to get started | The walkthrough | Help | Debugging | Functions | Target construction | Packages | Projects | Data and files | Literate programming | Distributed computing | Performance | Dynamic branching | Static branching

Getting Identifiers | Retrieving Chemical Properties

Introduction to doctest | Adding doctests to your package | Tags | @doctest | @expect | @expectRaw | @snap | @testRaw | @omit and @resume | @doctestExample | Caveats | Writing good doctests | Related packages

Definitions | Examples | c() and vctrs::vec_c() | Atomic vectors | Incompatible vectors and non-vectors | Factors | Date-times | Dates and date-times | Missing values | Data frames | Matrices and arrays | Implementation | ifelse()

You can use break names for labels | close_end works differently | close_end is TRUE by default | New raw parameter for chop() | Other changes | Feedback

Multiple models | References

Multiple models | More information

Prerequisites | Using in a tibble | Fixing the data type | Custom rendering | Truncation | Adaptive rendering | Testing

July 2020 Update | Introduction | Default (Weakly Informative) Prior Distributions | Default priors and scale adjustments | Regression coefficients | Intercept | Auxiliary parameters | Note on data-based priors | Disabling prior scale adjustments | How to Specify Flat Priors (and why you typically shouldn't) | Uninformative is usually unwarranted and unrealistic (flat is frequently frivolous and fictional) | Specifying flat priors | Informative Prior Distributions

Preamble | Introduction | Technical details | Model formulation | Longitudinal submodel(s) | Event submodel | Association structures | Assumptions | Log posterior distribution | Model predictions | Individual-specific predictions for in-sample individuals (for $0 \leq t \leq T_i$) | Individual-specific predictions for in-sample individuals (for $t > C_i$) | Individual-specific predictions for out-of-sample individuals (i.e. dynamic predictions) | Population-level (i.e. marginal) predictions | Standardised survival probabilities | Model extensions | Delayed entry (left-truncation) | Multilevel clustering | Model comparison | LOO/WAIC in the context of joint models | Usage examples | Dataset used in the examples | Fitting the models | Univariate joint model (current value association structure) | Univariate joint model (current value and current slope association structure) | Multivariate joint model (current value association structures) | Posterior predictions | Predicted individual-specific longitudinal trajectory for in-sample individuals | Predicted individual-specific survival curves for in-sample individuals | Combined plot of longitudinal trajectories and survival curves | Predicted individual-specific longitudinal trajectory and survival curve for out-of-sample individuals (i.e. dynamic predictions) | Predicted population-level longitudinal trajectory | Standardised survival curves | References

Step 1: ShinyApps account | Step 2: Use deploy_shinystan to deploy your app to shinyapps.io

Introduction | GLMs with group-specific terms | Priors on covariance matrices | Overview | Details | Comparison with lme4 | Advantage: better uncertainty estimates | Advantage: incorporate prior information | Disadvantage: speed | Relationship to glmer | Relationship to gamm4 | Relationship to nlmer | Conclusion

Using the ShinyStan app with different types of objects | stanfit objects | stanreg and brmsfit objects | mcmc.list objects | Other types of objects | 3-D array | List of matrices | Other functions in the shinystan package | Generating new quantities | Storing your model code in a shinystan object | Renaming a model

The Data | Exploring Graphically | Comparing sample to population | Effect of the post-stratification variable on preference for cats | Interaction effect | Design effect | Population Estimate | Estimates for states | Other formats | Alternate methods of modelling | Appendix | Examples of other formulas | Code to simulate the data | References

Introduction | Likelihood | Priors | Example | Conclusion

Introduction | Likelihood | Priors | Posterior | An Example Using Simulated Data | An Example Using Gasoline Data | References

Introduction | Likelihood | QR Decomposition | Priors | Posterior | Example | Alternative Approach | Conclusion | References

Introduction | Likelihood | Priors | Example | Conclusion

Executing a C function later | Background tasks