Maps of the Provinces, Cantons and Parishes of Ecuador (Mapas de Provincias, Cantones y Parroquias del Ecuador)

Provides the boundaries of the provinces, cantons and parishes of Ecuador as 'sf' objects, identified by the codes of the national political-administrative division (DPA, "División Político-Administrativa"), ready to be mapped and joined with health, epidemiological surveillance or other data. The three levels nest exactly and are offered in two resolutions: a light one for maps and dashboards and a detailed one for spatial analysis. The layers are derived from the open data of the Territorial Organization of the State published by the National Committee of Internal Boundaries (CONALI, Ministry of Government of Ecuador) under a CC BY licence < https://www.datosabiertos.gob.ec/>, and are downloaded on first use as 'GeoPackage' files, verified and cached locally. Includes a catalogue of DPA codes, a table to update codes from earlier versions, helpers to clean codes and place names, and tools to relocate the Galapagos Islands in national maps. Documentation is in Spanish.

Tools for Storing, Restoring and Searching for R Objects

Data exploration and modelling is a process in which a lot of data artifacts are produced. Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. The more projects we work with the more artifacts are produced and the harder it is to manage these artifacts. Archivist helps to store and manage artifacts created in R. Archivist allows you to store selected artifacts as a binary files together with their metadata and relations. Archivist allows to share artifacts with others, either through shared folder or github. Archivist allows to look for already created artifacts by using it's class, name, date of the creation or other properties. Makes it easy to restore such artifacts. Archivist allows to check if new artifact is the exact copy that was produced some time ago. That might be useful either for testing or caching.

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.

Access and Work with HCUP Resources and Datasets

A comprehensive R package for accessing and working with publicly available and free resources from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). The package provides streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) mapping files and Summary Trend Tables, enabling researchers and analysts to efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to CCSR categories and access HCUP statistical reports. Key features include: direct download from HCUP website, multiple output formats (long/wide/default), cross-classification support, version management, citation generation, and intelligent caching. The package does not redistribute HCUP data files but facilitates direct download from the official HCUP website, ensuring users always have access to the latest versions and maintain compliance with HCUP data use policies. This package only accesses free public tools and reports; it does NOT access HCUP databases (NIS, KID, SID, NEDS, etc.) that require purchase. For more information, see < https://hcup-us.ahrq.gov/>.

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>.

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.

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'.