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

Using credentials to access S3 buckets | Creating an S3 client

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 | 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

Setup | Random walk on weeks | Integrated AR | Stationary AR | Gaussian process on weeks | Independent per-(week, group) effects | Day-of-week effects | Combining latent and periodic terms | Moving-average components | Fit diagnostics | When to reach for which

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

Use case | Other tools for delay estimation | Two ways to estimate a delay | Getting set up | Simulating a reporting triangle | Fitting the full model | Fitting the delay-only model | Comparing the recovered delay parameters | Comparing the recovered delay distribution | Recovering the delay from data with missing cells | Using the delay-only estimate to set priors | When to use the delay-only model | 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

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

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

Public S3 Buckets | Private S3 Buckets | Session Info

Motivation | Using grumpy | Structured datatypes

Data types | Dimensions

Scope | FAQ | Why not use reticulate?

Setup | The pipeline workflow | Assigning age groups | Surveys | Bootstrapping | Demography | 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

Quick start | Package | Data | Filtering | Preprocessing | Visualising the data | Choosing a nowcast horizon | Nowcast target | The default model | Posterior predictions | Alternative models | Process model | Reference model: reporting delays | Fitting the alternative models | Results | Diagnostics | Comparing all models | Using package functions rather than S3 methods | Next steps

Packages | Data | Approach 1: Weekly timestep | Approach 2: Daily process, weekly reporting (fitted day-of-week) | Approach 3: Daily process, weekly reporting (structural) | Approach 4: Daily timestep | Comparison | Weekly scale | Daily scale | Runtime | Choosing an approach

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

Overview | Setup | Model specifications | Fitting | NUTS with prior initialisation (default) | NUTS with pathfinder initialisation | Pathfinder (approximate inference) | Runtime comparison | Diagnostics | NUTS diagnostics | Pathfinder diagnostics | Nowcast comparison | Posterior parameter comparison | Updating with posterior-as-prior | Summary

Use case | What we have | What do we do | Getting setup | Introducing the data: COVID-19 hospitalisations in Germany | Overview | Data transformations | Filtering the data | Visualising the data | Model | Expected hospitalisations | Expected infections | Instantaneous reproduction number | Latent infections | Latent reporting delay and ascertainment | Specifying the model using epinowcast::enw_expectation() | Delay distribution | Defining the delay distribution | Specifying the model using epinowcast::enw_reference() | Observation model and nowcast | Defining the observation model | Specifying the model using epinowcast::enw_obs() | Fitting the model to COVID-19 hospitalisations in Germany | Preprocess the data | Fitting the epinowcast model | Specifying the fitting options | Compiling the model | Fitting the model | Visualising the Nowcast | Plotting the nowcast based on real-time data | Plotting the nowcast based on retrospective data | Posterior predictions for cases by date of positive test and report | Real-time and retrospective estimates of the effective reproduction number | Estimates of the delay from testing positive to hospitalisation both in real-time and retrospectively | Estimates of the number of expected hospitalisations both in real-time and retrospectively | Wrapping up | Summary | Strengths | Limitations | Alternative packages | References

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

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 | 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

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

Univariate forecasts | Multivariate forecasts | Multivariate point forecasts

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

Packages | Data | Data preprocessing | Models | Shared reporting delay distribution | Using the inflated posterior as a prior | Reference day of the week effect | Posterior predictions | Reporting day of the week effect | Age group variation | Variation based on reference date | Variation based on reference date stratified by age | Independent models for each age group. | Alternative models | Evaluation | Summary

Introduction | 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 | 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 | Wormbase | Phytozome | Version 12 | Version 13 | Session Info

Introduction | Selecting an Ensembl BioMart database and dataset | Step1: Identifying the database you need | Step 2: Choosing a dataset | Ensembl mirror sites | Using archived versions of Ensembl | Using Ensembl Genomes | How to build a biomaRt query | Searching for filters and attributes | Using predefined filter values | Finding out more information on filters | filterType | Attribute Pages | Using select() | Result Caching | biomaRt helper functions | exportFASTA | Examples of biomaRt queries | Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes | Annotate a set of EntrezGene identifiers with GO annotation | Retrieve all HUGO gene symbols of genes that are located on chromosomes 17,20 or Y, and are associated with specific GO terms | Annotate set of idenfiers with INTERPRO protein domain identifiers | Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chromosome 16 between basepair 1100000 and 1250000. | Retrieve all EntrezGene identifiers and HUGO gene symbols of genes which have a "MAP kinase activity" GO term associated with it. | Given a set of EntrezGene identifiers, retrieve 100bp upstream promoter sequences | Retrieve all 5' UTR sequences of all genes that are located on chromosome 3 between the positions 185,514,033 and 185,535,839 | Retrieve protein sequences for a given list of EntrezGene identifiers | Retrieve known SNPs located on the human chromosome 8 between positions 148350 and 148400 | Given the human gene TP53, retrieve the human chromosomal location of this gene and also retrieve the chromosomal location and RefSeq id of its homolog in mouse. | Connection troubleshooting | r BiocStyle::Biocpkg("biomaRt") specific solutions | Global connection settings | Error: "SSL certificate problem" | Error: "sslv3 alert handshake failure" | Session Info

Dependencies

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