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Access 'Office for Budget Responsibility' Data
Provides clean, tidy access to data published by the 'Office for Budget Responsibility' ('OBR'), the UK's independent fiscal watchdog. Covers the Public Finances Databank (outturn for PSNB, PSND, receipts, and expenditure since 1946), the Historical Official Forecasts Database (every 'OBR' forecast since 2010), the Economic and Fiscal Outlook detailed forecast tables (five-year projections from the latest Budget), the monthly profiles for the public finances (the 'OBR' forecast apportioned across the months of the fiscal year), and the Welfare Trends Report (incapacity benefit spending and caseloads). All returned objects carry provenance metadata recording the source URL, publication vintage, retrieval time, and file fingerprint, so analyses can be audited and reproduced. Data is downloaded from the 'OBR' on first use and cached locally for subsequent calls. Data is sourced from the 'OBR' website < https://obr.uk>.
Spatial Data Analysis
Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on < https://rspatial.org/> to get started.
Read and write PNG images
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
Read and Write 'FreeSurfer' Neuroimaging File Formats
Provides functions to read and write neuroimaging data in various file formats, with a focus on 'FreeSurfer' formats. This includes, but is not limited to, the following file formats: 1) MGH/MGZ/NIFTI format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) 'FreeSurfer' morphometry data files in binary 'curv' format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas. 4) Surface file format. Contains a brain surface mesh, given by a list of vertices and a list of faces.
SQLite Interface for R
Embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine and for various extensions is included. System libraries will never be consulted because this package relies on static linking for the plugins it includes; this also ensures a consistent experience across all installations. Optionally, when libcurl is available at build time, an experimental HTTP/HTTPS virtual file system (VFS) can be enabled to allow read-only access to remote immutable SQLite database files via URIs.
Cross-Platform 'zip' Compression
Cross-Platform 'zip' Compression Library. A replacement for the 'zip' function, that does not require any additional external tools on any platform.
Cache and Retrieve Computation Results
Easily cache and retrieve computation results. The package works seamlessly across interactive R sessions, R scripts and Rmarkdown documents.
'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library
'Armadillo' is a templated C++ linear algebra library aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab. It is useful for algorithm development directly in C++, or quick conversion of research code into production environments. It provides efficient classes for vectors, matrices and cubes where dense and sparse matrices are supported. Integer, floating point and complex numbers are supported. A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency. Dynamic evaluation automatically chooses optimal code paths based on detected matrix structures. Matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can automatically use 'OpenMP' multi-threading (parallelisation) to speed up computationally expensive operations. The 'RcppArmadillo' package includes the header files from the 'Armadillo' library; users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. Starting from release 15.0.0, the minimum compilation standard is C++14. Since release 7.800.0, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Data Source Catalogues Online for Southern Ocean Ecosystem Research
Obtains lists of files of remote sensing collections for Southern Ocean surface
properties. Commonly used data sources of sea surface temperature, sea ice concentration, and
altimetry products such as sea surface height and sea surface currents are cached in object storage
on the Pawsey Supercomputing Research Centre facility. Patterns of working to retrieve data from these object storage
catalogues are described. The catalogues include complete collections of datasets Reynolds et al. (2008)
"NOAA Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1"
Lightweight and Feature Complete Unit Testing Framework
Provides a lightweight (zero-dependency) and easy to use unit testing framework. Main features: install tests with the package. Test results are treated as data that can be stored and manipulated. Test files are R scripts interspersed with test commands, that can be programmed over. Fully automated build-install-test sequence for packages. Skip tests when not run locally (e.g. on CRAN). Flexible and configurable output printing. Compare computed output with output stored with the package. Run tests in parallel. Extensible by other packages. Report side effects.