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r-corrplot
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public |
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-gmisc
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public |
Tools for making the descriptive "Table 1" used in medical articles, a transition plot for showing changes between categories, flow charts by extending the grid package, a method for variable selection based on the SVD, Bézier lines with arrows complementing the ones in the 'grid' package, and more.
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2025-03-25 |
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r-forestplot
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A forest plot that allows for multiple confidence intervals per row, custom fonts for each text element, custom confidence intervals, text mixed with expressions, and more. The aim is to extend the use of forest plots beyond meta-analyses. This is a more general version of the original 'rmeta' package's forestplot() function and relies heavily on the 'grid' package.
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2025-03-25 |
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r-elasticnet
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Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for estimating sparse Principal Components. The Lasso solution paths can be computed by the same function. First version: 2005-10.
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2025-03-25 |
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r-entropy
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This package implements various estimators of entropy, such as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator. It also offers an R interface to the NSB estimator. Furthermore, it provides functions for estimating Kullback-Leibler divergence, chi-squared, mutual information, and chi-squared statistic of independence. In addition there are functions for discretizing continuous random variables.
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2025-03-25 |
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r-bbmisc
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Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development.
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2025-03-25 |
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r-mlr
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Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.
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2025-03-25 |
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r-parallelmap
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Unified parallelization framework for multiple back-end, designed for internal package and interactive usage. The main operation is a parallel "map" over lists. Supports local, multicore, mpi and BatchJobs mode. Allows "tagging" of the parallel operation with a level name that can be later selected by the user to switch on parallel execution for exactly this operation.
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2025-03-25 |
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r-paramhelpers
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Functions for parameter descriptions and operations in black-box optimization, tuning and machine learning. Parameters can be described (type, constraints, defaults, etc.), combined to parameter sets and can in general be programmed on. A useful OptPath object (archive) to log function evaluations is also provided.
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2025-03-25 |
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r-fastmatch
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Package providing a fast match() replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match() function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory.
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2025-03-25 |
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r-rsample
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Classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).
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2025-03-25 |
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r-generics
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In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics.
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2025-03-25 |
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r-furrr
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public |
Implementations of the family of map() functions from 'purrr' that can be resolved using any 'future'-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.
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2025-03-25 |
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r-future
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The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures.
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2025-03-25 |
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r-globals
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Identifies global ("unknown" or "free") objects in R expressions by code inspection using various strategies, e.g. conservative or liberal. The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in distributed compute environments.
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2025-03-25 |
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r-listenv
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List environments are environments that have list-like properties. For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.
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2025-03-25 |
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r-ranger
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A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.
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2025-03-25 |
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r-tcltk2
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A series of additional Tcl commands and Tk widgets with style and various functions (under Windows: DDE exchange, access to the registry and icon manipulation) to supplement the tcltk package
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2025-03-25 |
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r-timereg
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Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package.
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2025-03-25 |
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r-iswr
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Data sets and scripts for text examples and exercises in P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
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2025-03-25 |
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r-matching
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public |
Provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided.
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2025-03-25 |
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r-optmatch
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Distance based bipartite matching using the RELAX-IV minimum cost flow solver, oriented to matching of treatment and control groups in observational studies. Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.
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2025-03-25 |
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r-ritools
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Tools for randomization inference.
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2025-03-25 |
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r-matchit
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Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>.
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2025-03-25 |
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r-svd
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R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
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2025-03-25 |
