This package performs penalized multivariate analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis.

This package provides tools to create a measure of inter-point dissimilarity useful for clustering mixed data, and, optionally, perform the clustering.

This package is a feature selection package of the mlr3 ecosystem. It selects the optimal feature set for any mlr3 learner. The package works with several optimization algorithms e.g. random search, Recursive feature elimination, and genetic search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.

Learn vector representations of sentences, paragraphs or documents by using the Paragraph Vector algorithms, namely the distributed bag of words (PV-DBOW) and the distributed memory (PV-DM) model. Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering. It does this by embedding documents in the semantic space as defined by the doc2vec algorithm. Next it maps these document embeddings to a lower-dimensional space using the Uniform Manifold Approximation and Projection (UMAP) clustering algorithm and finds dense areas in that space using a Hierarchical Density-Based Clustering technique (HDBSCAN). These dense areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can be found which are representative of the topic.

This package generates ROC plots. Most ROC curve plots obscure the cutoff values and inhibit interpretation and comparison of multiple curves. This attempts to address those shortcomings by providing plotting and interactive tools. Functions are provided to generate an interactive ROC curve plot for web use, and print versions. A Shiny application implementing the functions is also included.

This is an unofficial package aimed at automating the import of LISREL output in R.

This package includes functions to compute the area under the curve of selected measures: the area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). The curves can also be visualized. Support for partial areas is provided.

This package contains functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set cosmesis), and for censored data with competing risks (see data set menopause). The package also provides functions to visualize the observed data and the MLE.

This package provides resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010) and complementary pairs stability selection with improved error bounds (Shah & Samworth, 2013) are implemented. The package can be combined with arbitrary user specified variable selection approaches.

This package provides advanced tryCatch and try functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).

Machine Learning 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 interpretability. The DALEX package contains various explainers that help to understand the link between input variables and model output.

Colored terminal output on terminals that support ANSI color and highlight codes. It also works in Emacs ESS. ANSI color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the "chalk" JavaScript project.

This package provides functions and data to construct technical trading rules with R.

The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.

Ggplot2 is an implementation of the grammar of graphics in R. It combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, and you can still build up a plot step by step from multiple data sources. It also implements a sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes.

Provides implementations of functions which have been introduced in R since version 3.0.0. The backports are conditionally exported which results in R resolving the function names to the version shipped with R (if available) and uses the implemented backports as fallback. This way package developers can make use of the new functions without worrying about the minimum required R version.

This package provides the full texts for Jane Austen's six completed novels, ready for text analysis. These novels are "Sense and Sensibility", "Pride and Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion".

Tools to clean and process text. Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards (2001) doi:10.1006/csla.2001.0169) or extracting them into new variables. For example, emoticons are often used in text but not always easily handled by analysis algorithms. The replace_emoticon() function replaces emoticons with word equivalents.

ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.

This package provides functions for Bayesian A/B testing including prior elicitation options based on Kass and Vaidyanathan (1992) doi:10.1111/j.2517-6161.1992.tb01868.x.

This package provides routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

This package provides an interface to Amazon Web Services database services, including Relational Database Service (RDS), DynamoDB NoSQL database, and more.

This package implements S4 classes and various tools for financial time series. Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions are provided.

This package checks adherence to a given style, syntax errors and possible semantic issues. It supports on the fly checking of R code edited with RStudio IDE, Emacs and Vim.