collapse - Advanced and Fast Data Transformation

A large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R, fast functions for data transformation and common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as 'units', 'integer64', 'xts'/ 'zoo', 'tibble', 'grouped_df', 'data.table', 'sf', and 'pseries'/'pdata.frame'. For a concise overview of the package see Krantz (2026) <doi:10.18637/jss.v116.i01>.

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data-aggregationdata-analysisdata-manipulationdata-processingdata-sciencedata-transformationeconometricshigh-performancepanel-datascientific-computingstatisticstime-seriesweightedweightscppopenmp

15.93 score 696 stars 133 dependents 1.6k scripts 49k downloads

kit - Data Manipulation Functions Implemented in C

Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.

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openmp

10.64 score 61 stars 6 dependents 130 scripts 6.1k downloads

fastverse - A Suite of High-Performance Packages for Statistics and Data Manipulation

Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. The core 'fastverse' consists of 4 packages: 'data.table', 'collapse', 'kit' and 'magrittr', that jointly only depend on 'Rcpp'. The 'fastverse' can be freely and permanently extended with additional packages, both globally or for individual projects. Separate package verses can also be created. Fast packages for many common tasks such as time series, dates and times, strings, spatial data, statistics, data serialization, larger-than-memory processing, and compilation of R code are listed in the README file: <https://github.com/fastverse/fastverse#suggested-extensions>.

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ccppdata-aggregationdata-manipulationdata-sciencedata-transformationhigh-performancelow-dependencypanel-datastatistical-computingtime-seriesweights

9.71 score 320 stars 254 scripts 4.3k downloads

dggridR - Discrete Global Grids

Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.

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discrete-global-gridsgeospatialspatial-analysiscpp

9.58 score 186 stars 1 dependents 858 scripts 592 downloads

dfms - Dynamic Factor Models

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

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dynamic-factor-modelstime-seriesopenblascpp

6.65 score 45 stars 25 scripts 526 downloads

flownet - Transport Modeling: Network Processing, Route Enumeration, and Traffic Assignment

High-performance tools for transport modeling - network processing, route enumeration, and traffic assignment in R. The package implements the Path-Sized Logit model for traffic assignment - Ben-Akiva and Bierlaire (1999) <doi:10.1007/978-1-4615-5203-1_2> - an efficient route enumeration algorithm, and provides powerful utility functions for (multimodal) network generation, consolidation/contraction, and/or simplification. The user is expected to provide a transport network (either a graph or collection of linestrings) and an origin-destination (OD) matrix of trade/traffic flows. Maintained by transport consultants at CPCS (cpcs.ca).

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openmp

6.20 score 14 stars 14 scripts 479 downloads

osmclass - Classify Open Street Map Features

Classify Open Street Map (OSM) features into meaningful functional or analytical categories. Designed for OSM PBF files, e.g. from <https://download.geofabrik.de/> imported as spatial data frames. A classification consists of a list of categories that are related to certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main osm_classify() function returns a classification data table giving, for each feature, the primary and alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. The package also contains a classification of OSM features by economic function/significance, following Krantz (2023) <https://www.ssrn.com/abstract=4537867>.

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3.30 score 2 stars 8 scripts 486 downloads

samadb - South Africa Macroeconomic Database API

An R API providing access to a relational database with macroeconomic time series data for South Africa, obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via the EconData <https://www.econdata.co.za/> platform and automated scraping of the SARB and STATSSA websites. The database is maintained at the Department of Economics at Stellenbosch University.

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1.00 score 2 scripts 201 downloads

africamonitor - Africa Macroeconomic Monitor Database API

An R API providing access to a relational database with macroeconomic data for Africa. The database contains >700 macroeconomic time series from mostly international sources, grouped into 50 macroeconomic and development-related topics. Series are carefully selected on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. The project is part of the 'Kiel Institute Africa Initiative' <https://www.ifw-kiel.de/institute/initiatives/kiel-institute-africa-initiative/>, which, amongst other things, aims to develop a parsimonious database with highly relevant indicators to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform at <https://africamonitor.ifw-kiel.de/>. The database is maintained at the Kiel Institute for the World Economy <https://www.ifw-kiel.de/>.

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1.00 score 2 scripts 296 downloads

ugatsdb - Uganda Time Series Database API

An R API providing easy access to a relational database with macroeconomic, financial and development related time series data for Uganda. Overall more than 5000 series at varying frequency (daily, monthly, quarterly, annual in fiscal or calendar years) can be accessed through the API. The data is provided by the Bank of Uganda, the Ugandan Ministry of Finance, Planning and Economic Development, the IMF and the World Bank. The database is being updated once a month.

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1.00 score 9 scripts 317 downloads