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r-dendextend
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Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.
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2025-03-25 |
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r-deepboost
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Provides deep boosting models training, evaluation, predicting and hyper parameter optimising using grid search and cross validation. Based on Google's Deep Boosting algorithm, and Google's C++ implementation. Cortes, C., Mohri, M., & Syed, U. (2014) <http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>.
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2025-03-25 |
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r-cubist
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Regression modeling using rules with added instance-based corrections.
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2025-03-25 |
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r-crosstalk
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Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files). Currently supports linked brushing and filtering.
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2025-03-25 |
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r-cramer
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Provides R routine for the so called two-sample Cramer-Test. This nonparametric two-sample-test on equality of the underlying distributions can be applied to multivariate data as well as univariate data. It offers two possibilities to approximate the critical value both of which are included in this package.
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2025-03-25 |
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r-cpe
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Functions to calculate concordance probability estimates in survival analysis.
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2025-03-25 |
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r-cowplot
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Some helpful extensions and modifications to the 'ggplot2' package. In particular, this package makes it easy to combine multiple 'ggplot2' plots into one and label them with letters, e.g. A, B, C, etc., as is often required for scientific publications. The package also provides a streamlined and clean theme that is used in the Wilke lab, hence the package name, which stands for Claus O. Wilke's plot package.
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2025-03-25 |
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r-covr
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Track and report code coverage for your package and (optionally) upload the results to a coverage service like 'Codecov' <http://codecov.io> or 'Coveralls' <http://coveralls.io>. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code.
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2025-03-25 |
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r-corrplot
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A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.
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2025-03-25 |
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r-corpcor
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Implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The details of the method are explained in Schafer and Strimmer (2005) <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252>. The approach is both computationally as well as statistically very efficient, it is applicable to "small n, large p" data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix.
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2025-03-25 |
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r-commonmark
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The CommonMark specification defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text.
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2025-03-25 |
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r-combinat
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routines for combinatorics
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2025-03-25 |
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r-colourpicker
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A colour picker that can be used as an input in Shiny apps or Rmarkdown documents. The colour picker supports alpha opacity, custom colour palettes, and many more options. A Plot Colour Helper tool is available as an RStudio Addin, which helps you pick colours to use in your plots. A more generic Colour Picker RStudio Addin is also provided to let you select colours to use in your R code.
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2025-03-25 |
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r-codetools
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Code analysis tools for R.
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2025-03-25 |
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r-cmprsk
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Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509.
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2025-03-25 |
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r-ckmeans.1d.dp
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Fast optimal univariate clustering and segementation by dynamic programming. Three types of problem including univariate k-means, k-median, and k-segments are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of within-cluster distances using respective metrics. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. Weighted k-means and unweighted k-segments algorithms can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. In contrast to heuristic methods, this package provides a powerful set of tools for univariate data analysis with guaranteed optimality. Use four spaces when indenting paragraphs within the Description.
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2025-03-25 |
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r-checkmate
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Tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead.
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2025-03-25 |
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r-catpredi
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Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) <doi:10.1177/0962280215601873>. I Barrio, MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) <https://www.idescat.cat/sort/sort411/41.1.3.barrio-etal.pdf>.
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2025-03-25 |
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r-caretensemble
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Functions for creating ensembles of caret models: caretList and caretStack. caretList is a convenience function for fitting multiple caret::train models to the same dataset. caretStack will make linear or non-linear combinations of these models, using a caret::train model as a meta-model, and caretEnsemble will make a robust linear combination of models using a glm.
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2025-03-25 |
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r-cardata
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Datasets to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage (forthcoming).
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2025-03-25 |
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r-car
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Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, in press.
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2025-03-25 |
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r-c50
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C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).
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2025-03-25 |
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r-bst
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Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.
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2025-03-25 |
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r-bsda
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Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.
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2025-03-25 |
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r-broom
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Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
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2025-03-25 |
