Preamble | Installation | Introduction | Representation | Handling | Accession | Subsetting | Internals | Annotations | Transformations | Utilities | Cropping | Masking | Querying | Combining | Coordinates | Appendix | Resources | Session info | References

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

Introduction to Zarr | Storage size comparison | Decoding speed comparison

Interface | New species / speciesL argument | New version argument

Overview | Installation and loading | Discover supported attributes | Retrieve gene annotations | Retrieve transcript-level annotations | Use the getGene() compatibility wrapper | Retrieve sequences | Find orthologues | Migrating common r BiocStyle::Biocpkg("biomaRt") code | Roadmap and future direction | Session information

Interface | New species / speciesL argument | New version argument

Overview | Installation and loading | Discover supported attributes | Retrieve gene annotations | Retrieve transcript-level annotations | Use the getGene() compatibility wrapper | Retrieve sequences | Find orthologues | Migrating common r BiocStyle::Biocpkg("biomaRt") code | Roadmap and future direction | Session information

Interface | New species / speciesL argument | New version argument

Overview | Installation and loading | Discover supported attributes | Retrieve gene annotations | Retrieve transcript-level annotations | Use the getGene() compatibility wrapper | Retrieve sequences | Find orthologues | Migrating common r BiocStyle::Biocpkg("biomaRt") code | Roadmap and future direction | Session information

Installation | Introduction | Visualization | Images | Labels | Points | Shapes | Layering | Scalebars | Session info

Setup | The pipeline workflow | Assigning age groups | Surveys | A note on truncated surveys | Bootstrapping | Demography | When your population is coarser than your age groups | Symmetric contact matrices | Contact rates per capita | Splitting contact matrices | Filtering | Participant weights | Temporal aspects and demography | User-defined participant weights | Weight threshold | Numerical example | Get survey data | Weight by day of week | Weight by age | Apply threshold | Plotting | Using ggplot2 | Using R base | References

Available distributions | Discretisation and adjustment for maximum delay | Double interval censoring with primarycensored

Introduction | Decomposition into expected final notifications and report delay components | Expected final notifications | Default model | Generalised model | Instantaneous reproduction number/growth rate | Latent infections/notifications | Latent reporting delay and ascertainment | Delay distribution | Parametric baseline hazard | Non-parametric reference date effect $\delta_{g,t,d}$ and report date effect $\epsilon_{g,t,d}$ | Centring | Observation model and nowcast | Accounting for reported cases with a missing reference date | Implementation | Summary of module-parameter mappings | References

Overview | Core Capabilities | Different Timesteps and Timespans | Stratified and Multi-Group Nowcasting | Delay Modelling Approaches | Report Date Effects and Structural Reporting | Latent Process Models | Hierarchical Structure | Prior Specification | Missing Data Handling | Model Evaluation | Visualisation | Computational Options | Data Handling | Current Limitations | Further Reading

Epinowcast and Stan | Ensuring you have the proper toolchain | Now install CmdStanR and CmdStan | Epinowcast modelling | Installation | Running your first model | Setting enw_fit_opts | Investigating the quality of the model fit | Sampler settings | chains | threads_per_chain | iter_warmup and iter_sampling | max_treedepth | adapt_delta | Some decent defaults | Model settings | Setting priors | Exploring your data | Posterior predictions | Approaches to solve common problems | My model takes too long to run | Divergent transitions | My $\hat{R}$s are high and my esss are low | The posterior estimates are very wide | Other resources | Technical issues | Learning more about Stan and Bayesian inference

Introduction | What an ARIMA term does | Using ARIMA terms in a formula | The kernel decomposition | Centring the integrated residual | Stationarity via partial autocorrelations | Group structure: dependent vs independent | Special cases | Priors | Where you can use it | Limitations | References

Introduction | What a Gaussian process term does | Using Gaussian process terms in a formula | The kernel and Hilbert-space decomposition | Kernels and special cases | Group structure: dependent vs independent | Differencing order | Priors | Where you can use it | Limitations | References

Motivation | Using grumpy | Structured datatypes

NumPy format versions | Data types | Dimensions | Pickled objects

Using credentials to access S3 buckets | Creating an S3 client

Using credentials to access S3 buckets | Creating an S3 client

Univariate forecasts | Multivariate forecasts | Multivariate point forecasts | Multivariate quantile forecasts | Comparing the energy and variogram scores

Function names, signatures, and return values | FAQ | Why a new package?

Function names, signatures, and return values | FAQ | Why a new package?

Function names, signatures, and return values | FAQ | Why a new package?

Zarr version | Reading and Writing | Stores | Data Types | Codecs | Compression codecs | Other codecs | Optional fields or features

Zarr version | Reading and Writing | Stores | Data Types | Codecs | Compression codecs | Other codecs | Optional fields or features

Introduction | Installation of the HDF5 package | High level R-HDF5 functions | Creating an HDF5 file and group hierarchy | Writing and reading objects | Writing and reading objects with file, group and dataset handles | Writing and reading with subsetting, chunking and compression | Influence of chunk size and compression level | Saving multiple objects to an HDF5 file (h5save) | List the content of an HDF5 file | Dump the content of an HDF5 file | Reading HDF5 files with external software | Removing content from an HDF5 file | 64-bit integers | Low level HDF5 functions | Creating an HDF5 file and a group hierarchy | Writing data to an HDF5 file | Session Info

Introduction | Reading subsets of data | Using the index argument | Using hyperslab selections | Irregular selections | Using hyperslab selection tools | Slowdown when selecting unions of hyperslabs | Summary | Writing in parallel | Example data | Serial writing of datasets | Parallel writing of datasets | Session info

Diagnosing missingness | Scoring | Filtering to a common set of targets | Requiring partial coverage | Filtering to a specific model's targets | Imputing missing scores | NA | Worst score | Reference model | Mean score | Combining filter and impute

Introduction | Limitations with Rarr | Example data | Quick start guide | Additional details | Working with Zarr metadata | Writing subsets of data | Creating an "empty" array | Updating a subset of an existing array | Appendix | Session info

Introduction | Limitations with Rarr | Example data | Quick start guide | Additional details | Working with Zarr metadata | Writing subsets of data | Creating an "empty" array | Updating a subset of an existing array | Appendix | Session info

Introduction | romeo | What is OME-Zarr? | Installation | Reading OME-Zarr files | Images | Labels | Reading from S3 storage | Writing OME-Zarr files | Appendix | Session info

Introduction | romeo | What is OME-Zarr? | Installation | Reading OME-Zarr files | Images | Labels | Reading from S3 storage | Writing OME-Zarr files | Appendix | Session info

Public S3 Buckets | Private S3 Buckets | Session Info

Setup | Data | Preprocessing | Latest observations | Cumulative reporting delay | Reporting delay heatmap | Reporting delay quantiles | Notifications by delay group | Using the individual plot functions | Helper functions

Guiding principles | Scope | Zarr version | Functional programming and API design

Guiding principles | Scope | Zarr version | Functional programming and API design

Overview | Preparations | Example data | Executability of the code | Alignment | Preparing the annotation | Counting reads | Building a DEXSeqDataSet | Standard analysis workflow | Loading and inspecting the example data | Normalisation | Dispersion estimation | Testing for differential exon usage | Additional technical or experimental variables | Visualization | Parallelization and large number of samples | Perform a standard differential exon usage analysis in one command | Appendix | Controlling FDR at the gene level | Preprocessing with python | Preparing the annotation file with python | Counting reads with python | Reading the data from the python ouputs into R | Preprocessing using featureCounts | Further accessors | Overlap operations | Methodological changes since publication of the paper | Requirements on GTF files | Session Information | References

Introduction | Install and load the package | ZarrArray objects | Construction | Array and matrix operations | Other operations | Write an array-like object to disk in Zarr format | Session information

Introduction | Metrics for point forecasts | A note of caution | Absolute error | Squared error | Absolute percentage error | Binary forecasts | Brier score | Logarithmic score | Sample-based forecasts | CRPS | Overprediction, underprediction and dispersion | Log score | Dawid-Sebastiani score | Dispersion - Median Absolute Deviation (MAD) | Bias | Absolute error of the median | Squared error of the mean | Quantile-based forecasts | Weighted interval score (WIS) | Interval coverage | Interval coverage deviation | Quantile score | Additional metrics | Quantile coverage

Running code in parallel | Performance comparison

Other packages to compute FD indices | Main functions | Benchmark between packages | Functional Dispersion (FDis) | Functional Divergence (FDiv) | Functional Evenness (FEve) | Functional Richness (FRic) | Functional Richness Intersection (FRic_intersect) | Rao's Quadratic Entropy (Q) | Benchmark within fundiversity | Increasing the number of species | Functional Richness | Functional Divergence | Rao's Quadratic Entropy | Functional Evenness | Comparing between indices | Increasing the number of sites | References

Introduction | Basic use | Affymetrix data - before preprocessing | Affymetrix data - after preprocessing | ExpressionSet and ExpressionSetIllumina | Two colour arrays, NChannelSet, RGList, MAList | Loading data from ArrayExpress | Making the report more informative by adding a factor of interest | Extended use | Spatial layout of the array | Mapping of the reporters | RNA quality | Session Info | References

Introduction | Data preparation | Module generating functions | Outlier detection | Rendering the report | Session Info

Motivation | Usage | Link to the library | Locating the library headers | Configuration arguments for non-standard system setups | Funding | Session info

Dependencies

Scope | Naming conventions | Input/Output/Interoperability | Design decisions | Dependencies | Development journey

How to restrict the possible parameter range?

Scope | Architecture | Naming conventions

Step 1: import un-normalised data | Step 2: find the matching white reference | Step 3: normalise the reflectance data

Scope | Architecture | Naming conventions

Step 1: import un-normalised data | Step 2: find the matching white reference | Step 3: normalise the reflectance data

coda | bayesplot

Can estimations go beyond outside the range of the inits? | How to restrict the possible parameter range?

Import spectral data: lr_get_spec() | Import spectral metadata: lr_get_metadata() | Convert spectral data to csv: lr_convert_tocsv()

Comparison of outputs | Example of analysis

Import spectral data: lr_get_spec() | Import spectral metadata: lr_get_metadata() | Convert spectral data to csv: lr_convert_tocsv()

Comparison of outputs | Example of analysis

Scope | Naming conventions | Input/Output/Interoperability | Design decisions | Dependencies

Extracting R package author information | Extracting R package author information from the Author field | Extracting R package author information from the Authors@R field | Cleaning and deduplicating author names | Harmonize differently abbreviated names

Scope of regulations | Data privacy and integrity | Internet access | Registration and third parties | Updates

Motivations | linelist in a nutshell | Outline | Should I use linelist? | Getting started | Installation | Key functionalities | Tagging system | Validation | Secured methods | Worked example | Example dataset | Creating a linelist object | Changing tags | Accessing tagged variables | Using safeguards | Changing tag loss action permanently

Verbs operating on rows | dplyr::arrange() ✅ | dplyr:distinct() ✅ | dplyr::filter() ✅ | dplyr::slice() ✅ | Verbs operating on columns | dplyr::mutate() ✓ (partial) | dplyr::pull() ✅ | dplyr::relocate() ✅ | dplyr::rename() & dplyr::rename_with() ✅ | dplyr::select() ✅ | Verbs operating on groups ✘ | Verbs operating on data.frames | dplyr::bind_rows() ✅ | dplyr::bind_cols() ✘ | Joins ✘ | Verbs operating on multiple columns | dplyr::pick() ✘

Scope | Input/Output/Interoperability | Design decisions | Dependencies

Motivation | Usage | With rhdf5 | Writing data | Reading data | With external applications | h5dump example | Compiling the compression libraries | Session info

Required data | Functional Richness (FRic) - fd_fric() | Functional volume intersect (FRic_intersect) - fd_fric_intersect() | Functional Divergence (FDiv) - fd_fdiv() | Functional Evenness (FEve) - fd_feve() | Functional Dispersion (FDis) - fd_fdis() | Rao's Quadratic Entropy (Q) - fd_raoq() | Large site-species data / sparse matrices | Standardizing trait data | Non-continuous traits? | Missing values in traits? | Functions summary table | References

Why do I need to pass a list of files to parse rather than an entire folder?

Changes of abundance | Changes of coordinates (= traits)

Scope | Dependencies | Functions | Each index should be computed in its own separate function | Input data should not be transformed without any explicit action from the user | Inputs | Outputs | Functions should output data.frames | The outputs of functions should be similar in structure