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r-datawizard
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A lightweight package to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. It is also the data wrangling backend for packages in 'easystats' ecosystem. References: Patil et al. (2022) <doi:10.21105/joss.04684>.
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2025-04-22 |
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r-dataset
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The aim of the 'dataset' package is to make tidy datasets easier to release, exchange and reuse. It organizes and formats data frame 'R' objects into well-referenced, well-described, interoperable datasets into release and reuse ready form. A subjective interpretation of the W3C DataSet recommendation and the datacube model <https://www.w3.org/TR/vocab-data-cube/>, which is also used in the global Statistical Data and Metadata eXchange standards, the application of the connected Dublin Core <https://www.dublincore.org/specifications/dublin-core/dcmi-terms/> and DataCite <https://support.datacite.org/docs/datacite-metadata-schema-44/> standards preferred by European open science repositories to improve the findability, accessibility, interoperability and reusability of the datasets.
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2025-04-22 |
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r-dataretrieval
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Collection of functions to help retrieve U.S. Geological Survey and U.S. Environmental Protection Agency water quality and hydrology data from web services. Data are discovered from National Water Information System <https://waterservices.usgs.gov/> and <https://waterdata.usgs.gov/nwis>. Water quality data are obtained from the Water Quality Portal <https://www.waterqualitydata.us/>.
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2025-04-22 |
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r-datapreparation
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Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of 'data.table' efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.
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2025-04-22 |
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r-datapasta
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RStudio addins and R functions that make copy-pasting vectors and tables to text painless.
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2025-04-22 |
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r-datamods
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'Shiny' modules to import data into an application or 'addin' from various sources, and to manipulate them after that.
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2025-04-22 |
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r-datamaid
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Data screening is an important first step of any statistical analysis. dataMaid auto generates a customizable data report with a thorough summary of the checks and the results that a human can use to identify possible errors. It provides an extendable suite of test for common potential errors in a dataset.
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2025-04-22 |
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r-dataeditr
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An interactive editor built on 'rhandsontable' to allow the interactive viewing, entering, filtering and editing of data in R <https://dillonhammill.github.io/DataEditR/>.
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2025-04-22 |
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r-dataexplorer
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Automated data exploration process for analytic tasks and predictive modeling, so that users could focus on understanding data and extracting insights. The package scans and analyzes each variable, and visualizes them with typical graphical techniques. Common data processing methods are also available to treat and format data.
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2025-04-22 |
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r-datacombine
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Tools for combining and cleaning data sets, particularly with grouped and time series data.
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2025-04-22 |
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r-databaseconnector
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An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server', 'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', 'Snowflake', 'Spark', and 'SQLite'). Also includes support for fetching data as 'Andromeda' objects. Uses either 'Java Database Connectivity' ('JDBC') or other 'DBI' drivers to connect to databases.
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2025-04-22 |
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r-data.validator
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Validate dataset by columns and rows using convenient predicates inspired by 'assertr' package. Generate good looking HTML report or print console output to display in logs of your data processing pipeline.
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2025-04-22 |
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r-dashboardthemes
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Allows manual creation of themes and logos to be used in applications created using the 'shinydashboard' package. Removes the need to change the underlying css code by wrapping it into a set of convenient R functions.
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2025-04-22 |
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r-dartr.data
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Data package for 'dartR'. Provides data sets to run examples in 'dartR'. This was necessary due to the size limit imposed by 'CRAN'. The data in 'dartR.data' is needed to run the examples provided in the 'dartR' functions. All available data sets are either based on actual data (but reduced in size) and/or simulated data sets to allow the fast execution of examples and demonstration of the functions.
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2025-04-22 |
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r-dalextra
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Provides wrapper of various machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the interpretable machine learning, there are more and more new ideas for explaining black-box models, that are implemented in 'R'. 'DALEXtra' creates 'DALEX' Biecek (2018) <arXiv:1806.08915> explainer for many type of models including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. Important part of the package is Champion-Challenger analysis and innovative approach to model performance across subsets of test data presented in Funnel Plot.
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2025-04-22 |
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r-dalex
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Any unverified black box model is the path to failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore the model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) <arXiv:1806.08915>.
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2025-04-22 |
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r-daewr
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Contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.
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2025-04-22 |
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r-dagitty
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A port of the web-based software 'DAGitty', available at <http://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.
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2025-04-22 |
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r-dae
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The content falls into the following groupings: (i) Data, (ii) Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v) Matrix functions, (vi) Projector and canonical efficiency functions, and (vii) Miscellaneous functions. There is a vignette describing how to use the design functions for randomizing and assessing designs available as a vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of information when the 'Error' function has been used in the call to 'aov'. The package 'dae' can also be installed from <http://chris.brien.name/rpackages/>.
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2025-04-22 |
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r-d3tree
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Create and customize interactive collapsible 'D3' trees using the 'D3' JavaScript library and the 'htmlwidgets' package. These trees can be used directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents. When in Shiny the tree layout is observed by the server and can be used as a reactive filter of structured data.
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2025-04-22 |
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r-dabestr
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Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. <doi:10.1038/s41592-019-0470-3>. The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.
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2025-04-22 |
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r-d3r
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Provides a suite of functions to help ease the use of 'd3.js' in R. These helpers include 'htmltools::htmlDependency' functions, hierarchy builders, and conversion tools for 'partykit', 'igraph,' 'table', and 'data.frame' R objects into the 'JSON' that 'd3.js' expects.
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2025-04-22 |
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r-cvms
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Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).
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2025-04-22 |
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r-cvar
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Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator or probability density function. ES is also known as Conditional Value at Risk (CVaR). Virtually any continuous distribution can be specified. The functions are vectorized over the arguments. The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002) <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided, as well.
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2025-04-22 |
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r-cubelyr
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An implementation of a data cube extracted out of 'dplyr' for backward compatibility.
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2025-04-22 |
