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Spatiotemporal Arrays, Raster and Vector Data Cubes
Reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL' bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.
API Wrapper for Nasdaq Data Link
Functions for interacting directly with the Nasdaq Data Link API to offer data in a number of formats usable in R, downloading a zip with all data from a Nasdaq Data Link database, and the ability to search. This R package uses the Nasdaq Data Link API. For more information go to < https://docs.data.nasdaq.com/>. For more help on the package itself go to < https://data.nasdaq.com/tools/r>.
Multivariate Exploratory Data Analysis and Data Mining
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Visualizing Categorical Data
Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).
Linear Models for Panel Data
A set of estimators for models and (robust) covariance matrices, and tests for panel data
econometrics, including within/fixed effects, random effects, between, first-difference,
nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models,
panel generalized method of moments (GMM) and general FGLS models,
mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators
with common factors, variable coefficients and limited dependent variables models.
Test functions include model specification, serial correlation, cross-sectional dependence,
panel unit root and panel Granger (non-)causality. Typical references are general econometrics
text books such as Baltagi (2021), Econometric Analysis of Panel Data (
'Global Data Lab' R API
Retrieve datasets from the 'Global Data Lab' website < https://globaldatalab.org> directly into R data frames. Functions are provided to reference available options (indicators, levels, countries, regions) as well.
Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files
Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files. This package replaces the former ncdf package, which only worked with netcdf version 3 files. For various reasons the names of the functions have had to be changed from the names in the ncdf package. The old ncdf package is still available at the URL given below, if you need to have backward compatibility. It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem. However, the ncdf package does not provide an interface for netcdf version 4 files.
A Wrapper of the JavaScript Library 'DataTables'
Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'.
Quantitative Analysis of Textual Data
A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
Official R API for Fetching Data from 'EODHD'
Second and backward-incompatible version of R package 'eodhd' < https://eodhd.com/>, extended with a cache and quota system, also offering functions for cleaning and aggregating the financial data.