What this vignette is for | Setup: simulate one predictor, one response — over years | Stage 1: turn a quick frequentist fit into a prior | Stage 2: Bayesian refit with the informed prior | Stage 3: posterior prediction across the forecast horizon | Stage 4: decompose the variance over time | Stage 5: when does the forecast become uninformative? | The whole-pipeline figure | What this example does not show | Session info

Introduction | The simulated dataset | Setup | Exploring the dataset | Visualise the training data | Single-cluster workflow: Cluster A | Step 1 — Elastic net for prior extraction | Step 2 — Extract informed priors | Step 3 — Fit the Bayesian model | Step 4 — Diagnose convergence and model fit | Step 5 — Predict with environmental noise propagation | Step 6 — Uncertainty decomposition | Step 7 — Forecast shelf life | Step 8 — Calibration assessment | Visualisations | Forecast fan chart | Prior versus posterior | Forest plot: Bayesian vs. elastic net | Parameter recovery validation | Multi-cluster grouped workflow | Fitting | Diagnostics | Prediction and decomposition | Shelf life comparison | Calibration and forecast plots | Long-horizon forecasting with GCM projections | Using lm instead of glmnet | Standardising and back-transforming | Summary | Session information

Location | Clearing | Projects

Very Quick Guide | Setup tokens | Explore Outputs! | Go Further by Generating Datasets! | Framework

REDCap API tokens | 1. Setting Your Token using User Environment Variables (preferred) | 2. Setting Your Token using keyring package | 3. Setting Your Token for One Session | Testing Your Token (optional) | Additional Resources

Getting Started | Creating a Tremendous API Client | Send Rewards | Perform GET Requests | Perform POST Requests | Perform DELETE Requests

Другие схемы авторизации | Обновление токена доступа

Краткое описание. | Установка пакета rmytarget. | Пример кода для загрузки данных из API MyTarget | Работа с обычным рекламным аккаунтом, даже если вы имете к нему доступ через агентский аккаунт | Работа с агентским аккаунтом | Получение списка клиентов для агентского аккаунта. | Получение списка рекламных кампаний. | Получение списка объявлений. | Получение статистики по рекламным аккаунтам и объявлениям. | Группы метрик которые можно задавать в аргументе metrics:

Example on real data | Explore the data | Deciding the contrasts and the random effects part | Fitting the BMSC model | The summary.BMSC output | Understanding the summary.BMSC output | Checking the diagnostic indexes | The Control Group results | The differences between the Control Group and the Single Case | The Body District : Congruency interaction: | The Body District : Congruency : Side interaction: | Efficient approximate leave-one-out cross-validation for fitted BMSC models | A binomial case | Conclusions | References

Introduccion | Población adulto mayor en la Región de los Ríos | Graficar comunas | Graficar provincias | Graficar regiones | Graficar zonas | Ejercicios para el usuario

1. Introduction | 2. Term Structure Estimation | 3. The N-Factor Model | 4. Using the 'NFCP' package: | 4.1 Stitching Futures Data | 4.2 Filtering commodity pricing models: | 4.2.1 Complete, Stitched Data: | 4.2.2 Incomplete, Full Contract Data: | 4.3 Measurement Error of N-Factor Commodity Pricing Models: | 4.4 Estimating N-Factor Commodity Pricing Models: | 4.5 N-Factor Model Comparison: | 4.6 N-Factor Model Term Structure Fit: | 4.7 Plot Contract Observation Error: | 4.8 Plot filtered values: | 4.9 Plot Standard Deviation of State Variables: | 4.10 Forecast Spot and Futures Prices: | 4.10.1 Spot Prices: | 4.10.2 Futures Prices | 4.10.3 Plot the Futures Curve: | 4.11 Plot Volatility Term Structure: | 4.12 Simulate Spot and Futures Prices: | 4.12.1 Spot Prices: | 4.12.2 Futures Prices: | 4.13 Option Valuation: | 5. Discussion: | References:

Examples | References

Overview | Quickstart | Map | Basemaps | Sources | Layers | Popups and tooltips | Controls | Expressions | Shiny Bindings

1. Load inputs | 2. Initiate cluster | 3. Run simulations in parallel | Error handling | 4. Collect results from nodes | 5. Summarize with dplyr

Utilização | Nota <a href="https://www.ipea.gov.br"><img src="../man/figures/ipea_logo.png" alt="Ipea" align="right" width="300"/></a>

Introduction | Installation | Loading a CLDF Dataset | Exploring a CLDF Dataset | Accessing the data. | Load all the source information | Construct a 'wide' table with all foreign key entries filled in: | Load just one table: | Get the citation for a dataset: | Quickly get information on an unloaded dataset: | Easily get reference catalogs (Glottolog, Concepticon): | Finding Datasets | Cache Information | Using rcldf to analyse datasets: | References:

Introduction to the rbrsa package | Installation and Setup | Part 1: Discovering Available Data | Monthly Bulletin Tables | Finturk Tables | Part 2: Fetching Monthly Bulletin Data | Part 3: Fetching Granular Finturk Data | Part 4: Saving Your Results | Next Steps

Algorithms | Water Quality Functions | wq_calc() | Single Algorithm | Multiple Algorithms | Type of Algorithm | All Algorithms | Mapping Functions | Map_WQ_raster | Raster with points | Modeling Functions | extract_lm | Example | extract_lm_cv | extract_lm_cv_multi | extract_lm_cv_all | Acknowledgements | Credit

Jump right in | Running sampler | Choose the source data file | Choose the data worksheet | Choose the number of backups | Choose the sampling type | Choose what to stratify on | Output | Installation | Other necessary packages | Examples

Example 1 | Map displaying Percentage of School Drop-outs from Total Respondents in each Region | Example 2 | Map displaying the Regional Population Density of School Drop-outs

Record R code as it is typed | Input a character string of R code | Input a .R file

Introduction | Load libraries | Baobab trees of Madagascar | Environmmental variables | Make training samples | Fit the model | Make the predictions under current and future environment | Environmental response curves | Preciction wrapper function | Environmental response maps | Analyze environmetnal contribution of observations | Affects of changing environment

Set up | Prepare environmental variables | Creating the virtual species | Generate pseudo samples for virtual species | Build a simple isolation_forest species distribution model | Analyze variable dependence | Analyze variable contribution

Set up | Prepare environmental variables | Prepare occurrence from GBIF | Understand the correlations between variables | Split occurrence to training and test | Build a isolation_forest species distribution model | Visualize results | Response curves | Variable importance | Presence-absence map | Analyze variable dependence | Analyze variable contribution | Conclusion

1. Overview | 2. Attribute naming convention | 2.1. Variable names | 2.2. Temporal stratification | 2.3. Function names (and optional parameters) | 2.4. Operator name | 2.5. Viewing attribute definitions and calculating values | 2.6. Multivariable attributes | 3. Creating the exposure space (Step A) | 3.1. Perturbed attributes | 3.2. Held attributes | 3.3. Tied attributes | 3.4 Example: Creating an exposure space | 4. Generating perturbed climate scenarios (Step B) | 4.1. The inverse optimization approach | 4.2. Generating scenarios with generateScenarios() | 4.3. Stochastic simulation options (the control file) | 4.3.1. Control file format | 4.3.2. Stochastic weather generators | 4.3.3. Post-processing of stochastic weather generator output | 4.3.4. Optimization | Objective function weights | Parameter Bounds | Optimization arguments | 4.3.5. Advice for managing the number of parameters and target attributes | 4.3.6. Example: Generating perturbed stochastic climates | 5. Evaluation of perturbed climates | 5.1. Evaluation of target attributes | 5.2. Evaluating changes in attributes | 5.3. Evaluating the ability of SWGs to capture climate features relevant to the system model | 6. Calculate and evaluate performance (Steps C and D) | 6.1. Plotting OAT (One-At-a-Time) performance | 6.2. Plot 2D performance spaces: | 7. Conclusion | Appendix: Scott Creek case study | References

1. Introduction | 1.1. Objectives and application areas of foreSIGHT | 1.2. foreSIGHT workflow for climate stress testing | 2. Case Study - Climate 'Stress-test' of a Rainwater Tank System | 2.1. Step A: Identify attributes for perturbation and create an exposure space | 2.2. Step B: Generate perturbed time series | 2.3. Step C: Simulate system performance | 2.4. Step D: Visualise system performance | 2.5 Step E: Evaluate system options | 3. Advanced usage | 3.1 Stochastic simulation | 3.2 Coupling with external system models | 4. Conclusions | 5. References

Local explanations | Global explanations | Parallel processing

I. Illustration using rat tumor data (Binomial Family) | Pre-Inferential Modeling | MacroInference | MicroInference | Finite Bayes | II. Comparison of $\mathcal{L}^2$ and maximum entropy representations using galaxy data (Normal Family) | III. Illustration using arsenic data (Normal Family) | IV. Illustration using child illness data (Poisson Family)

Introduction | Calculating slopes | Measures of route hilliness | Segments in a route: Cumulative slope | References

Performance

Extract the OSM network from geofabrik | Clean the road network | Filter the unconnected segments | Break the segments on vertices | Get slope values for each segment | Result: | Other examples

Installation | Installation for DEM downloads | Functions | Elevation | Slope calculation | Plotting | Helper functions | Examples | Add elevation to a linestring | Calculate slope | Plot elevation profile | Working with segments

Introduction | Comparison with results from ArcMap 3D Analyst | Three-dimensional traces of roads dataset | References

Introduction | Reading GEXF files | Creating GEXF files

VBTree is designed for what | Batch data processing through array or tensor | Advanced batch data processing thorugh vector binary tree | Advantage of VBTree

Main functions | Input data | Conceptual knowledge about VBTree data frame | Auto plots for stress-strain curves | Applying Kalman smoothing | Adiabatic heating correction | Extraction based on given strain | Automatic calculation | Preparation for visualization | 2D processing map visualization | 3D processing map visualization

Searching for and using inactive concepts in SNOMED CT | Creating SNOMED CT codelists from scratch | Converting Read codelists to SNOMED CT | HTML codelist for this example | More information

Creating a concept database for MiADE and MedCAT | SNOMED CT concept decomposition | More information

Basic introduction to SNOMED CT | Loading the SNOMED CT dictionaries | Using R environments | SNOMED CT concepts IDs in R | Set operations using SNOMEDconcept | Using relationships between SNOMED CT concepts | Attributes of SNOMED CT concepts | SNOMED CT codelists | 'History of' SNOMED CT concepts | SNOMED CT simple refsets | Mapping between SNOMED CT and ICD-10 and OPCS4 | Mapping between SNOMED CT and Read Clinical Terminology | More information

Overview | Prerequisites | Install Ollama | Install R dependencies | Slash Commands | Available commands | Getting help for a command | Example: Monte Carlo simulation | Example: Chaining MCS to contingency | Example: Sensitivity analysis | Example: Earned Value Management | Example: Bayesian risk probability | Example: Second Moment Method | Input validation and guidance | Chat Interface | Using cloud models | Notes on chat reliability | Interactive Shiny App | Features | Configuration options | RAG Knowledge Base | Built-in knowledge files | How RAG context flows | Adding your own documents | Disabling RAG | Available Commands and Tools | Slash commands (deterministic) | LLM tools (via chat) | Evaluation with vitals | Troubleshooting | Tool calling not working | Slow responses | RAG build fails | Source citations missing

Introduction | Step 1: Prior Risk Probability | Step 2: Prior Cost Distribution | Step 3: Posterior Risk Probability (Bayesian Update) | Prior vs. Posterior Probability | Step 4: Posterior Cost Distribution | Prior vs. Posterior Cost Distribution | Summary

The Resource-Task Matrix | Parent DSM | The Risk-Resource Matrix | Grandparent DSM | Interpreting the DSM | References

Key Metrics | Example Setup | Planned Value (PV) | Earned Value (EV) | Actual Cost (AC) | Performance Indicators | Forecasting: Estimate at Completion (EAC) | EAC Comparison Table | Additional Metrics | Performance Trend Chart

Sigmoidal Models | Example: Fitting a Logistic Model | Fit the Model | Assess Fit Quality | Plot with Confidence Bands | Predict Future Completion | Comparing All Three Model Types | Summary

Steps in MC Simulation | Example | Correlation Matrix | Run the Simulation | Distribution of Outcomes | Interpreting Percentiles | Contingency Analysis | Sensitivity Analysis

When to Use SMM | Method Overview | Example | Implied Distribution and Confidence Interval | Comparison with Monte Carlo Simulation | Benefits and Limitations

Introduction | Installation | Installation of Python and its dependencies | Example Data Set Description | Load the data | Transform the data to the required input format | Create the individual layers data structure from the molecular data | Create inter-layer connections data structure | Create drug-target interaction data structure | Check input data structures | Run the complete pipeline | Run the individual pipeline steps | Step 1: Compute correlation matrices | Step 2: Generate individual graphs | Step 3: Combine graphs | Step 4: Identify drug targets and their edges | Step 5: Calculate integrated interaction score | Step 6: Generate differential graph | Step 7: Calculate differential drug response score | References

Introduction | The eurlex package | elx_make_query(): Generate SPARQL queries

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

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

Introduction | Installation | The rgam() function | Predictions | Plots and summaries | Cross-validation (CV) | RGAM for other families | Logistic regression with binary data | Poisson regression with count data | Cox regression with survival data

Relative risk v.s. odds ratio | Binary or continuous exposure variable | Nominal exposure variable | Multinomial logistic regression | Estimated variance of relative risk under binary response | Delta method | Estimated variance of relative risk under nominal response | Bootstrap | Examplary Data | Adjusted relative risks depending on confounders | multinomial logistic regression | Airquality example

Setup

Introduction | Examples | Continuous features and categorical target | Mixed targets and neighbor ranking | Neighbor ranking without targets | Logical features | Additional Information | Categorical features | Comparison measures | Distance | Similarity | Ties | Missing data | Neighbr and PMML | References

<a name="Introduction"></a> Introduction | <a name="Getting started"></a> Getting started | <a name="Getting help within R"></a> Getting help within R | <a name="Distribution list"></a> Distribution list | <a name="Data frames"></a> Data frames | <a name="Prey signature data"></a> Prey signature data | <a name="Predator signature data"></a> Predator signature data | <a name="Fatty acid suite data"></a> Fatty acid suite data | <a name="Preparing for an analysis"></a> Preparing for an analysis | <a name="Preparing fatty acid suite information"></a> Preparing fatty acid suite information | <a name="Preparing signature data"></a> Preparing signature data | <a name="Modifying invalid signature proportions"></a> Modifying invalid signature proportions | <a name="Scaling signatures"></a> Scaling signatures | <a name="Calibration coefficient for an augmented proportion"></a> Calibration coefficient for an augmented proportion | <a name="Distance measures"></a> Distance measures | <a name="Chi-square distance power parameter"></a> Chi-square distance power parameter | <a name="Estimation spaces"></a> Estimation spaces | <a name="Diet estimation"></a> Diet estimation | <a name="Estimating individual diet and variance"></a> Estimating individual diet and variance | <a name="Estimating mean diet and variance"></a> Estimating mean diet and variance | <a name="Adjusting for fat content"></a> Adjusting for fat content | <a name="Diagnostic functionality"></a> Diagnostic functionality | <a name="Exploring signatures for structure"></a> Exploring signatures for structure | <a name="Using a prey partition"></a> Using a prey partition | <a name="Pooling partitioned estimates to the original prey types"></a> Pooling partitioned estimates to the original prey types | <a name="Leave-one-prey-out analysis"></a>Leave-one-prey-out analysis | <a name="Pool leave-one-prey-out results to the original prey types"></a> Pool leave-one-prey-out results to the original prey types | <a name="Signature & calibration coefficient consistency"></a> Signature & calibration coefficient consistency | <a name="Goodness of fit"></a> Goodness of fit | <a name="Simulation functionality"></a> Simulation functionality | <a name="Predator diet composition"></a> Predator diet composition | <a name="Realistic diets"></a> Realistic diets | <a name="Random diets"></a> Random diets | <a name="Diet grid"></a> Diet grid | <a name="Simulating predator signatures"></a>Simulating predator signatures | <a name="Bootstrap sample size algorithm"></a> Bootstrap sample size algorithm | <a name="Generating a ghost prey signature"></a> Generating a ghost prey signature | <a name="Adding error to calibration coefficients"></a> Adding error to calibration coefficients | References

Basic information | Author and maintainer | Contributors | Citing this package | Getting help | Recent updates | Quick start example | Model specification | User-defined models | Built-in models | SIR | SIR with demography | SIR with vaccination at birth | SIS | SIS with demography | SIRS | SIRS with demography | SIRS with vaccination | More detailed examples | References

Introduction | Citing this package | Getting further information | Background | Guide to the mathematical model | Time to suppression | Implementation | Data preparation | Model fitting | Quick Start Example | Data exploration | Model fitting and output visualization | Additional functionality | References

sclink_norm | sclink_net | sclink_cor

Step 2 Basics | Types of Independent Variables | Need to Estimate All Parameters at Each Stage | Obtaining the Rank (rnk) | Skipping step 1 | Step 2 | A Consideration for Cox Proportional Hazard Calculations | Another consideration for Nonlinear Models | Extracting the Intermediate Statistics | Dependence on the User | Reference

Introduction | References

Simulating Data | Simulated Data | Generating the Stable Correlated Binomial Distribution | binomialRF Function Call | Tuning Parameters | Percent_features | ntrees

1 Introduction | 1.1 In-Buffer Calibration protocol: | 1.2 In-Sample Calibration protocol: | 2 Installation | 2.1 CRAN | 2.2 GitHub | 2.2.1 Install and load devtools | 2.2.2 Install ezmmek | 2.2.3 Required packages | 3 How to use | 3.1 Load ezmmek | 3.2 Example datasets | 3.2.1 Standard curve data | 3.2.2 Raw fluorescence data, IBC protocol | 3.2.3 Raw fluorescence data, ISC protocol | 3.3 Visible functions | 3.3.1 Create data.frame object of class new_ezmmek_sat_fit | 3.3.2 Create data.frame object of class new_ezmmek_calibrate | 3.3.3 Create data.frame object of class new_ezmmek_act_group | 3.3.4 Create data.frame object of class new_ezmmek_std_group | 4 Example analyses | 4.1 new_ezmmek_sat_fit | 4.1.1 new_ezmmek_sat_fit, IBC | 4.1.2 new_ezmmek_sat_fit, ISC | 4.2 new_ezmmek_act_calibrate | 4.2.1 new_ezmmek_act_calibrate, IBC | 4.2.2 new_ezmmek_act_calibrate, ISC | 4.3 new_ezmmek_act_group | 4.3.1 new_ezmmek_act_group, IBC | 4.3.2 new_ezmmek_act_group, ISC | 4.4 new_ezmmek_std_group | 4.4.1 new_ezmmek_std_group, IBC | 4.4.2 new_ezmmek_std_group, ISC | 5 Methods for new_ezmmek objects | 5.1 plot | 5.1.1 new_ezmmek_sat_fit, IBC and ISC | 5.1.2 new_ezmmek_calibrate, IBC and ISC | 5.1.3 new_ezmmek_act_group, IBC and ISC | 5.1.4 new_ezmmek_std_group, IBC and ISC | 6 References | 7 Authors | 8 License

Determinants | Objective | The detguide | Parsing the detguide: the symbolic representation | Challenges | Taking on the challenges | Discussion | Use of the functions included in the package | References

aquifer_param() arguments | aquifer_time() arguments | aquifer_predict() arguments | Installation | Examples | Example 1: Un-steady state radial flow, edge-water drive | Example 2: Un-steady state radial flow, bottom-water drive | Example 3: Pseudo-steady state radial flow, edge-water drive | Example 4: Un-steady state linear flow, edge-water drive | Example 5: Un-steady state linear flow, bottom-water drive | Example 6: Pseudo-steady state linear flow, edge-water drive | Example 7: Pseudo-steady state linear flow, bottom-water drive | Example 8: Un-steady state radial flow, edge-water drive | References

Example: | Example 2

Performing the Analysis | Rectification method 1. Three-point anisometric scaling (3-PAS) | Rectified Correlation | Analysis Step by Step | Calculate Distance | Calculate Weighted Distance Matrix | Moran's I | Resampling Method for I | Plotting Distribution (Optional) | Rectifying I | Rectification method 2. Pearson Correlation | Stability Analysis | Rectifying to "Max-Min" | Rectifying with correlation | References

What is GIFTr? | Aim of this document | Before you start. | How to start your quiz spreadsheet? | categories column | Questions column | Markdown and HTML Support | LATEX Support | Answers column | HTML, markdown and LATEX | MCQ | Numeric Entry | Short Answer Question | True or False | Answer Feedback | Question type column | Question names column | Import to R | GIFTr Function

Underlying Theory of the Test | Sources | Transformations and the Characterization | The support region for S | Implementing the Test | Relating Sample Size to the Size of Mini-cubes | Example Databases Included in MVNtestchar Package | Theorem 1. | Proof. | Theorem 2.

Client for the Alexa Web Information Services API | Installation | Usage | URL Information | Traffic History | Browse Categories | Category Listings | In Links

Overview | Basic Usage | Display Modes | Customizing Appearance | Integer Data | Large Samples | Teaching Features | Mean Line | DGP Overlay | Factor Input

The User-Item Rating Matrix

Dispatcher | The registry

Extending rrecsys (prototyping)

rrecsys | Installation & Loading the package

Item-based k-nearest neighbors in rrecsys

Non-personalized Recommendation in rrecsys

Simon Funk's Matrix Factorization

User-based k-nearest neighbors in rrecsys

Methodology | Getting started | An example for continuous target | References

Loading lodi and an example dataset | Implementing Censored Likelihood Multiple Imputation | Fit and pool outcomes models | Reference

Introduction | subgxe Example | Reference

Data | Phenotypes (Y) | Genotypes matrix (X) | "Kinship" matrix (K) | Y, X and K harmonization | Genetic or physical map | Association detection with the mlmm_allmodels function | Model selection | Estimation of the selected SNPs effects | Complete example script

Installation | Functions | Examples | Diagnostic Panels with resid_panel | Interactivity with resid_interact | Incorporating Predictor Variables with resid_xpanel | Model Comparisons with resid_compare | Additional Model Types with resid_auxpanel