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r-ecmwfr
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Programmatic interface to the European Centre for Medium-Range Weather Forecasts dataset web services (ECMWF; <https://www.ecmwf.int/>) and Copernicus's Climate Data Store (CDS; <https://cds.climate.copernicus.eu>). Allows for easy downloads of weather forecasts and climate reanalysis data in R.
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2025-04-22 |
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r-ecfun
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public |
Functions and vignettes to update data sets in 'Ecdat' and to create, manipulate, plot, and analyze those and similar data sets.
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2025-04-22 |
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r-echarts4r
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public |
Easily create interactive charts by leveraging the 'Echarts Javascript' library which includes 36 chart types, themes, 'Shiny' proxies and animations.
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2025-04-22 |
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r-ecdat
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Data sets for econometrics, including political science.
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2025-04-22 |
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r-ecb
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Provides an interface to the 'European Central Bank's Statistical Data Warehouse' API <https://sdw.ecb.europa.eu/>, allowing for programmatic retrieval of a vast quantity of statistical data.
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2025-04-22 |
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r-ebayesthresh
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Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package.
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2025-04-22 |
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r-easystats
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A meta-package that installs and loads a set of packages from 'easystats' ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, visualization, and reporting. Additionally, it provides articles targeted at instructors for teaching 'easystats', and a dashboard targeted at new R users for easily conducting statistical analysis by accessing summary results, model fit indices, and visualizations with minimal programming.
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2025-04-22 |
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r-easypackages
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Easily load and install multiple packages from different sources, including CRAN and GitHub. The libraries function allows you to load or attach multiple packages in the same function call. The packages function will load one or more packages, and install any packages that are not installed on your system (after prompting you). Also included is a from_import function that allows you to import specific functions from a package into the global environment.
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2025-04-22 |
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r-dynlm
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Dynamic linear models and time series regression.
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2025-04-22 |
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r-dynnom
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Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, mgcv::gam and gam::gam model objects.
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2025-04-22 |
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r-dslabs
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public |
Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.
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2025-04-22 |
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r-dsi
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'DataSHIELD' is an infrastructure and series of R packages that enables the remote and 'non-disclosive' analysis of sensitive research data. This package defines the API that is to be implemented by 'DataSHIELD' compliant data repositories.
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2025-04-22 |
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r-drdid
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Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.
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2025-04-22 |
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r-downloadthis
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Implement download buttons in HTML output from 'rmarkdown' without the need for 'runtime:shiny'.
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2025-04-22 |
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r-drc
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Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
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2025-04-22 |
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r-dplyrassist
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An RStudio addin for teaching and learning data manipulation using the 'dplyr' package. You can learn each steps of data manipulation by clicking your mouse without coding. You can get resultant data (as a 'tibble') and the code for data manipulation.
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2025-04-22 |
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r-dotwhisker
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Quick and easy dot-and-whisker plots of regression results.
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2025-04-22 |
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r-doubleml
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Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. 'DoubleML' allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the 'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on the 'R6' package is very flexible.
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2025-04-22 |
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r-domir
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Methods to apply decomposition-based relative importance analysis for R functions. This package supports the application of decomposition methods by providing 'lapply'- or 'Map'-like meta-functions that compute dominance analysis (Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>; Grömping, U. (2007) <doi:10.1198/000313007X188252>) or Shapley value regression (Lipovetsky, S., & Conklin, M. (2001) <doi:10.1002/asmb.446>) based on the values returned from other functions.
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2025-04-22 |
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r-dofuture
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The 'future' package provides a unifying parallelization framework for R that supports many parallel and distributed backends. The 'foreach' package provides a powerful API for iterating over an R expression in parallel. The 'doFuture' package brings the best of the two together. There are two alternative ways to use this package. The first is the traditional 'foreach' approach by registering the 'foreach' adapter 'registerDoFuture()' and so that 'y <- foreach(...) %dopar% { ... }' runs in parallelizes with the 'future' framework. The other alternative is to use 'y <- foreach(...) %dofuture% { ... }', which does not require using 'registerDoFuture()' and has many advantages over '%dopar%'.
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2025-04-22 |
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r-doebioresearch
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Performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples.
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2025-04-22 |
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r-doe.wrapper
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Various kinds of designs for (industrial) experiments can be created. The package uses, and sometimes enhances, design generation routines from other packages. So far, response surface designs from package 'rsm', Latin hypercube samples from packages 'lhs' and 'DiceDesign', and D-optimal designs from package 'AlgDesign' have been implemented.
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2025-04-22 |
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r-doe.base
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Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.
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2025-04-22 |
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r-dlookr
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A collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values and outliers and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and relationship between target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputates missing values and outliers, resolving skewness. And it creates automated reports that support these three tasks.
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2025-04-22 |
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r-dmwr2
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Functions and data accompanying the second edition of the book "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
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2025-04-22 |
