Executing a C function later | Background tasks

Reading web access logs | File reading | Splitting combined fields | Other ideas

Data simulation | Estimating $L$ with a fixed tuning parameter | Computing estimators along the a tuning parameter path | Selecting the tuning parameter

Introduction to Tabulog | Tabulog Templates | Parser Classes | Parser Formatters | An Example in R | Notes | Escape characters

1. Sampling uncertainty | 1.1 Sensitivity analysis for influential species | 1.1.1 influ_phylm(): Phylogenetic linear regression | 1.1.2. influ_physig(): Phylogenetic signal | 1.1.3 influ_continuous() and influ_discrete: Trait Evolution Continuous and Discrete Characters | 1.2. Sensitivity analysis for influential clades | 1.2.1 clade_phylm(): Phylogenetic linear regression | 1.2.2 clade_physig(): Phylogenetic signal | 1.2.3 clade_continuous() and clade_discrete: Trait Evolution Continuous and Discrete Characters | 1.3. Sensitivity analysis for sample size | 1.3.1 samp_phylm: Phylogenetic Linear regression | 1.3.2 samp_physig: Phylogenetic signal (k or lambda) | 1.3.3 samp_continuous() and samp_discrete: Trait Evolution Continuous and Discrete Characters | 2. Phylogenetic uncertainty | 2.1 tree_phylm: tree method for phylogenetic linear regression | 2.2 tree_physig(): tree method for phylogenetic signal | 2.3 tree_continuous() and tree_discrete: tree method for trait evolution of continuous and discrete characters | 3. Data uncertainty | 3.1 intra_phylm(): intra method for phylogenetic linear regression | 3.2 intra_physig(): intra method for phylogenetic signal | 4. Interacting uncertainties | Examples: | 4.1 tree_influ_phylm(): tree_influ method for phylogenetic linear regression | 4.2 tree_clade_phylm(): tree_clade method for phylogenetic linear regression | 4.3 tree_samp_phylm(): tree_samp method for phylogenetic linear regression | 4.4 tree_intra_phylm(): tree_intra method for phylogenetic linear regression | 5. Additional functions | 5.1 Combine data and phylogeny | 5.2 Phylogenetic signal for missing data | Background | Example: | 5.3 Diversification and speciation rates | 6. Using sensiPhy with other packages (caper, phytools, gls) | 6.1 PGLS regression | 6.2 phylogenetic signal | 7. How long does it take? | 8. How to support sensiPhy | 8.1 Report bugs or errors | 8.2 Small changes in sensiPhy documentation | 8.3 Contribute with code | 8.4 New ideas or suggestions? | 8.5 Code of Conduct

Bibliography

Bibliography

Purpose | Object oriented approach | Example | References

Simulation | Fitting an LCA model | Singular BIC for LCA | A simulation study | Bibliography

Simulating reduced rank regression data | Singular BIC for rank selection | Simulation study of rank selection | Bibliography

Introduction | Example using Z scores matrix as input | Example using two sided P-Values as input | Diagnostic Plots - how to identify model misfit in ztobins and twosided.PValues.tobins | Example for using clusters | Appendix: repfdr on clusters algorithm

Motivation | A Longer Example | Heuristics for Choosing the Plot Type | Other Features | Limitations | References

DP | Stacked Cigar Simulation | Trunk Simulation | Rotated Trunk Simulation

Writing New Classification Algorithms | Writing a Classification Method | Writing a prediction method | Using your Classification Technique for Embedding Evaluation

Writing New Embedding Algorithms | Embedding with your algorithm | Performing Cross-Validation with your Algorithm

LOL | Stacked Cigar Simulation | Trunk Simulation | Rotated Trunk Simulation

LR-CCA | Stacked Cigar Simulation | Trunk Simulation | Rotated Trunk Simulation

LRLDA | Stacked Cigar Simulation | Trunk Simulation | Rotated Trunk Simulation

PCA | Stacked Cigar Simulation | Trunk Simulation | Rotated Trunk Simulation

PLS | Stacked Cigar Simulation | Trunk Simulation | Rotated Trunk Simulation

LR-CCA | Stacked Cigar Simulation | Trunk Simulation | Rotated Trunk Simulation

Trunk, 2 Class | Rotated Trunk, 2 Class, non-equal priors | Trunk, 3 Class | Mean Difference | Toeplitz | QD- Toeplitz | XOR | Cigar | Fat Tails | Cross

Cross Validation to Determine Error of Embedding Alg composed with Classifier | Cross Validation to Determine Optimal Embedding Dimensions of Embedding Alg composed with Classifier

Running Online Bayesian Changepoint Detection | Generate Univariate Gaussian Data | Run Basic Online Changepoint Detection | Main Outputs | Additional Outputs | Online Changepoint Detection Function Options | Settings affecting speed: getR, truncRlim, optionalOutputs | Settings affecting truncation: truncRlim, maxRlength, minRlength | Settings affecting probability model: probModel, init_params | Gaussian Univariate | Gaussian Multivariate | Poisson Univariate | Gaussian and Poisson Multivariate | Missing Data Options

Installation | Look at the raw data | Fortify data | Estimate parameters | Plot results | Simulation of coverage | Design an experiment

Package overview | Required set-up to use this package | Mapping hurricane exposure | Plotting county-level continuous exposure | Mapping county-level binary exposure | Plotting storm tracks | Creating time series datasets of exposure | Handling multi-county communities

incidental | Data Format | Model Fitting | Model Outputs | Advanced Usage | Linear Extrapolation | Hyperparameter Tuning | Hyperparameter Selection Methods | Validation Likelihood Hyperparameter Selection

Implemented methods and functions | Usage of the TSrepr package | Bibliography

Clustering of time series representations | Bibliography

Overview | Section 1 - Preliminaries | 1-1 Load SparseMDC | 1-2 Real Data Example:Biase Data | 1-3 Data Formatting | Section 2 - Preprocessing | Section 3 - Penalty Parameter Estimation | 3-1 $\lambda_{1}$ | 3-2 $\lambda_{2}$ | Section 4 - SparseMDC | 4-1 Apply SparseMDC | 4-2 Examine Results | 4-3 Housekeeping Marker Genes | 4-4 Condition-Dependent/Condition-Specific Marker Genes | References

Introduction. | Volatility calculation. | Example of use.

fit.ssm | Constraints | Cross-validation

Introduction | Dice experiments | The function dice.roll | The function dice.combinations | The function coin.flip | Non-transitive dice | The function is.nonTransitive | The function nonTransitive.generator | Data sets

Setup | Basic use of Arena - Single Model, Global Explanations | Train a model | Create an explainer | Create an Arena and add the model | Run the live Arena server | Intermediate use of Arena - Single Model, Global and Local Explanations | Add local explanations | Advanced use of Arena - Multiple Models, Global and Local Explanations | Create more models | Create explainers | Add more models to the Arena | Serverless version of the Arena

Setup | Prepare models | Run arena | Custom observation names | Appending data

Setup | Prepare models | Prepare observations | Create arena | Appending data | New Arena session | Append to already existing session

Preface | Load data & libraries | Models | Create Explainers & Arena | Make explainers manualy

Introduction | Supported Microbenchmarks | Supported Dense Matrix Microbenchmarks | Supported Sparse Matrix Microbenchmarks | Supported Machine Learning Microbenchmarks | Support For Multithreading | Examples | Dense Matrix Microbenchmark Examples | Sparse Matrix Microbenchmark Examples | Machine Learning Microbenchmark Examples

Citation | Introduction | Requirements and input data | ASCII Grids of environmental variables | Maxent jar file | Files of occurrence and background locations | Workflow tutorial | maxent | outdir | gridfolder | occurrencelocations | backgroundlocations | additionalargs | contributionthreshold | correlationthreshold | betamultiplier | Variable selection procedure | Result files | References

Introduction | ERPs and impulsivity | Installing the package | Importing impulsivity ERP data | Plotting ERP data and effect curves | Multipanel plots of ERP curves | Plots of effect curves | Signal detection: significance of effect curves | Functional Analysis of Variance of ERP curves | Selecting significant effects in an ANOVA design | Fitted effect curves with the optimal model | Signal identification: significant time intervals | Averaging ERP curves in preselected time intervals | Control of the False Discovery Rate | The Guthrie-Buchwald procedure | The Adapative Factor-Adjustment method | Conclusions | References

Datasets: | Distances | Agglomerative Hierarchical Clustering | Agglomerative Hierarchical Clustering .DETAILS | Divisive Hierarchical Clustering | Divisive Hierarchical Clustering .DETAILS | Correlative Hierarchical Clustering | Correlative Hierarchical Clustering .DETAILS

Key definition of the neural fragility marker | Load small ictal ieeg data for patient PT01 | Compute Fragility Matrix | Predict the SOZ | Fragility Statistics | Plot functions for Fragility Matrix

Load the package and data | Data preprocess (Not included in the package) | Download Data | Remove Artifacts | Visualize Voltage Plot (Figure 2.b) | Compute the Fragility Matrix | Fragility Heatmap (Figure 2.a) | Mean and standard deviation statistics for SOZ and non SOZ electrode group (Figure 3.a) | Distribution of Fragility Values (Figure 3.b) | Comparing different lambda values (Figure 4)

Why ISCA? | What is ISCA? Some Background (adapted from Drouhot 2021) | Function / Step 1 | Function / Step 2 | Function / Step 3 | References

Use status_check() to assert whether the services are available | DAWA will be phased out | Change history

Getting regional data for Denmark | Using DAWA to crosstab municipalities and their regions. | Using DAWA map data

Quick start | Basics on non-local priors | Some default non-local priors | Variable selection for generalized additive models | Parameter estimation | Exact inference for block-diagonal regression | Model selection for mixtures

License | R | Introduction | Examples

Vignette for the Package: permRand | Example Use of Package Functions | Step 1: Generate a Test Dataset | Step 2: Format Datasets | Step 3: Randomize Dataset | Step 4: Check the Randomization | Step 5: Packing Lists From the Randomization Results | Step 6: Switching Without Re-Randomization | Step 7: Checking the Results | Step 8: Output the Packing Lists for the switchR Function

Introduction | 1. Decision Guide | Common surveys at a glance | 2. as_survey() — Taylor Series Designs | 2.1 Core arguments | 2.2 The nest argument | 2.3 The fpc argument | 2.4 Multi-level clustering | 2.5 Worked example: NHANES 2017–2018 | 2.6 Worked example: ANES 2024 | 2.7 Worked example: GSS 2024 | 2.8 Worked example: Pew NPORS 2025 | 3. as_survey_replicate() — Replicate Weight Designs | 3.1 The type argument | 3.2 Worked example: ACS PUMS 2022 — Wyoming | 3.3 Worked example: Pew Jewish Americans 2020 | 3.4 The scale and rscales arguments | 4. as_survey_twophase() — Two-Phase Designs | 4.1 What two-phase sampling is | 4.2 Arguments | 4.3 Worked example: National Wilms Tumor Study | 5. Simple Random Sample with as_survey() | 5.1 The fpc argument matters more here | 5.2 Worked example: School district survey | 6. as_survey_nonprob() — Non-Probability Samples | 6.1 The fundamental distinction | 6.2 Variance estimation: two modes | Choosing type | 6.3 What you can and cannot claim | 6.4 Worked example: Democracy Fund + UCLA Nationscape (SRS approximation) | 6.5 Worked example: Bootstrap replicate weights | 6.6 Worked example: University snowball sample | 7. When no constructor applies: convenience and purposive samples | 7.1 Example: program evaluation classrooms | 7.2 General decision rule | 8. Reference: Common Codebook Variables | References

Creating the survey object | as_survey() | as_survey_replicate() | as_survey_nonprob() | as_survey_twophase() | Analysis functions | Frequency tables — get_freqs() | Analyzing multiple variables at once | Weighted means — get_means() | Population totals — get_totals() | Weighted correlations — get_corr() | Ratio estimation — get_ratios() | Weighted quantiles — get_quantiles() | Treatment effects — get_diffs() | Two-sample t-test — get_t_test() | All-pairs comparisons — get_pairwise() | Population variance — get_variance() | Subgroup analysis — the group argument | Controlling uncertainty output | Regression | Summary

1. Creating Survey Design Objects | 1.1 Simple Random Sample | 1.2 Stratified Design | 1.3 Cluster Design | 1.4 Replicate Weights | 1.5 Calibrated / Non-Probability Samples | 1.6 Two-Phase Designs | 1.7 Constructor Summary | 2. Summary Statistics | 2.1 Weighted Means (Grouped) | 2.2 Proportions / Frequency Tables | 2.3 Population Totals | 2.4 Quantiles | 2.5 Ratios | 2.6 Correlations | 3. Controlling Uncertainty Output | 4. Features With No survey / srvyr Equivalent | 4.1 Automatic Value Labels | 4.2 Multiple Variables in One Call | 4.3 Minimum Cell Size Warnings | 4.4 Weighted Sample Size | 4.5 Metadata-Rich Results (.meta) | 5. Notable Differences | 6. Function Reference Table | 7. Learning More

Three-parameter Weibull | Profiling scale parameter β | Simplified log-likelihood | Partial Derivatives of simplified log-likelihood | Score equations: local extrema candidates | Weighted MLE (MLEw) | Parameter transformation | Log-based transformation | Logit-based transformation | Random right censoring | Exponential | Weibull | Implementation note | MLEc

Further readings

Introduction | Methodological framework | Using drda | Benchmarking | Summary and discussion

Overview | Functions and Input Formats | cALD | LDWrap | Input Formats | Haplotype Data | Genotype Data | Locus Order | LD.sign.test | LD.heat.map | Outputs | cALD() | Parameters | Examples

Overview | Input Data | Data Output | Error Messages and Codes | Known Issues | Parameters | Examples | Updating the bundled IMGT/HLA protein alignment | Updating BIGDAWG to latest developmental versions

Overview | Anatomy of a GL String | Input Data | Data Output | Parameters | Examples

Using shinyreprex | Reproducing the Environment | Registering Reactives Across Modules | Best Practices | Bind reprex_reactive call to Event | Register Reactives at Module Setup | Put Side-Effects in Observers | Create a Business Logic Package | Use Secrets in Reactives | Limitations | Only the Reactive Expression is Read | Non-Standard Evaluation is Best-Effort | Detected Packages Can Be a Superset

deparse_call() is more accurate | deparse_call() is clearer | deparse_call() fails rather than making things up

construct() and .cstr_construct() | .cstr_construct.() methods | opts_() function | is_corrupted_() function | Constructors | Attribute repair

Basic methods | Consuming / summarizing an iteror | Iterate over given data | Counting or other mathematically defined processes | Random number generation | Iterators dealing with files or functions | Custom iterators | Higher order iterator functions | Iterators as sequences -- Looping, repeating, indexing | Operating on data within iterators | Selection, filtering, limiting | Combining / splitting multiple iterators

Features | How is it different from iterators? | More reading | Installation | License

Introduction | Some Special Iterators

What methods are needed for an iteror? | Handling the end of iteration | Breaking out of a loop at end of iteration | Detecting end of iteration with a sentinel value | Unsafe sentinel values | Safe sentinel values | A simple iterator | A stateful iterator that stops | Using an iterator inside an iterator | A recycling iterator | Limiting infinite iterators | Conclusion

Fit the model | Plot the results | Comparing to built-in jackknife procedure

Feature selection algorithms | Filter methods | Wrapper methods | Embedded methods | Extracting the top models

Penalized classification algorithms to predict Species

A simple regression | Regularized regression estimators | A glimpse at the backend

Load the data | Model fitting | Plotting the results

Introduction | Derive log posterior and gradient for HMC | Linear mixed effects model example data | Comparison model - Frequentist | Fit model using hmc | Source | References

Introduction | Derive log posterior and gradient for HMC | Linear Regression Example Data | Comparison model - Frequentist | Fit model using hmc | MCMC summary and diagnostics | Source | References

Introduction | Derive log posterior and gradient for HMC | Logistic mixed effects model example data | QR decomposition of design matrix | Fit model using hmc | Comparison model - Frequentist | Source | References

Introduction | Derive log posterior and gradient for HMC | Logistic Regression Example Data | Comparison models - Frequentist and Metropolis-Hastings | Fit model using hmc | MCMC summary and diagnostics | Source | Reference

Introduction | Derive log posterior and gradient for HMC | Poisson Regression Example Data | Comparison model - Frequentist | Fit model using hmc | MCMC summary and diagnostics | Source | References

Package mathml in practice | Basic elements | Decorations | Operators and parentheses | Custom operators | Builtin functions | Custom functions | Indices and powers | Ringing back to R | Names and order of arguments | Matrices and Vectors | Short mathematical names for R symbols | Abbreviations | Context-dependent rendering | Conclusion | Appendix A: For package developers | Appendix B: Customizing the package | Implementation details | Linear models | (n)-th root | P-values | Acknowledgment | References

Data Preparation | Quality Control | Individual Specific Parameters | Inbreeding Coefficients | Kinship | Breed Composition | Kinship at Native Alleles | Population Specific Parameters | Genetic Diversity | Kinship and Diversity at Native Alleles | Native Effective Size | Effective Size | Change of Native Genome Equivalent and Ne | Change of Breed Composition | References

Data Preparation | Genotype File Format | Marker Map Format | Example Data Set | Individual Specific Parameters | Inbreeding Coefficients | Kinship | Genetic Distances | Haplotype Frequencies | Breed Composition | Kinship at Native Segments | Population Specific Parameters | Genetic Diversity | Kinship and Diversity at Native Segments | Multi-Breed Specific Parameters | Kinships Within and Between Breeds | Genetic Distances Between Breeds | Prioritizing Breeds for Conservation | References

Example Data Set | Introductory Example: Traditional OCS | Defining the Objective of a Breeding Program | Marker-based OCS | Maximize Genetic Gain | Minimize Inbreeding | Recover the Native Genetic Background | Increase Diversity Between Breeds | Pedigree-based OCS | Maximize Genetic Gain | Minimize Inbreeding | Recover the Native Genetic Background | References

Example dataset | Creating image collections from local image files | Creating a data cube | Aggregation and resampling | Data cube operations | Export data cubes to files | Parallel processing | Further reading

Overview | A Tutorial | Gaussian-based Model | A Creation of a model | Exploring a data set | Providing the set of parameters | Estimation | Refine estimation | Notes | Exponential-based Model | Exploring of a data set

Introduction | Define a variable block structure | Model selection | Train an HMM-VB model | Train an HMM-VB model with unknown variable block structure | Clustering with HMM-VB | Cluster alignment | Solution for slow data clustering with many changes in mode merging parameters | References

Perceptual Error Analysis | Setup: A Small Example Network | Reciprocity Errors | Imaginary Census | Individual Accuracy | Null Model Testing | Key Takeaways

Introduction | What Are Cognitive Social Structures? | Key Features | Creating Barry Graph Objects | From a List of Matrices | From a Block-Diagonal Matrix | Inspecting Graph Attributes

Imaginary Network Motifs: Testing Systematic Perception Errors | Setup | Data Overview | Creating the CSS Graphs | Observed Motif Counts | Null-Model Testing | Results | Full Summary | Tie-Level Accuracy | Conclusion

Krackhardt Advice Network | Data and Perceptions | Individual Accuracy | Perceptual Structure | Organizational Takeaways