dplyr is the next iteration of plyr. It is focused on tools for working with data frames. It has three main goals: 1) identify the most important data manipulation tools needed for data analysis and make them easy to use in R; 2) provide fast performance for in-memory data by writing key pieces of code in C++; 3) use the same code interface to work with data no matter where it is stored, whether in a data frame, a data table or database.

Extract metadata from NetCDF data sources; these can be files, file handles or servers. This package leverages and extends the lower level functions of the RNetCDF package providing a consistent set of functions that all return data frames.

This package lets you convert R Markdown documents and Jupyter notebooks to a variety of output formats using Quarto.

This package provides functions for Maximum Likelihood (ML) estimation, non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models.

This package provides a collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions.

Create and manage unique directories for each TensorFlow training run. This package provides a unique, time stamped directory for each run along with functions to retrieve the directory of the latest run or latest several runs.

This package provides a cross-platform Perl-based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX) files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains or can be specified by the user.

This package provides a hiredis wrapper that includes support for transactions, pipelining, blocking subscription, serialisation of all keys and values, Redis error handling with R errors. It includes an automatically generated R6 interface to the full hiredis API. Generated functions are faithful to the hiredis documentation while attempting to match R's argument semantics. Serialization must be explicitly done by the user, but both binary and text-mode serialisation is supported.

This package provides an improved implementation (based on k-nearest neighbors) of the density peak clustering algorithm, originally described by Alex Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets (> 100,000 samples) very efficiently.

This package lets you interact with Google Sheets through the Sheets API v4. This package can read and write both the metadata and the cell data in a Sheet.

This package provides utilities for secure password hashing via the argon2 algorithm.

This package provides tools to conditionally rotate or back-up files based on their size or the date of the last backup; inspired by the utility logrotate'.

This package provides some methods to manipulate HDF5 files, extending the hdf5r package. Reading and writing R objects to HDF5 formats follow the specification of AnnData.

This package provides a data frame to xlsx exporter based on libxlsxwriter.

This package provides a data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. vtreat prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems vtreat defends against: Inf, NA, too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training).

This package creates lots of colorful plots in a multitude of variations. Try a demo of the LSD by running demotour().

This package provides functions for assessing the replication/preservation of a network module's topology across datasets through permutation testing.

This package lets you assign distinct colors to arbitrary multi-dimensional data, considering its structure.

This is a package for text mining for word processing and sentiment analysis using dplyr, ggplot2, and other Tidy tools.

This package provides an exact Goodness-of-Fit test for multinomial data with fixed probabilities. It can be used to determine whether a set of counts fits a given expected ratio. To see whether a set of observed counts fits an expectation, one can examine all possible outcomes with xmulti() or a random sample of them with xmonte() and find the probability of an observation deviating from the expectation by at least as much as the observed. As a measure of deviation from the expected, one can use the log-likelihood ratio, the multinomial probability, or the classic chi-square statistic. A histogram of the test statistic can also be plotted and compared with the asymptotic curve.

This package computes two-sample confidence intervals for single, paired and independent proportions.

R's sf package ships with self-contained GDAL executables, including a bare bones interface to several GDAL-related utility programs collectively known as the GDAL utilities. For each of those utilities, this package provides an R wrapper whose formal arguments closely mirror those of the GDAL command line interface. The utilities operate on data stored in files and typically write their output to other files. Therefore, to process data stored in any of R's more common spatial formats (i.e. those supported by the sf and terra packages), first write them to disk, then process them with the package's wrapper functions before reading the outputted results back into R. GDAL function arguments introduced in GDAL version 3.5.2 or earlier are supported.

This package provides the functionality to set configuration options on a per-package basis. Options set by a given package only apply to that package, other packages are unaffected.

This package contains some functions to help users (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. Functions in this package are supposed to be efficient and easy to use.