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r-future.apply
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public |
Implementations of lapply(), sapply(), tapply(), vapply() and friends that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
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2025-03-25 |
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r-condensier
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public |
Non-Parametric Conditional Density Estimation
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2025-03-25 |
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r-delayed
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public |
A Framework for Parallelizing Dependent Tasks
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2025-03-25 |
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r-mixtools
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public |
Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
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2025-03-25 |
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r-segmented
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public |
Given a regression model, segmented `updates' the model by adding one or more segmented (i.e., piece-wise linear) relationships. Several variables with multiple breakpoints are allowed.
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2025-03-25 |
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r-mockery
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public |
The two main functionalities of this package are creating mock objects (functions) and selectively intercepting calls to a given function that originate in some other function. It can be used with any testing framework available for R. Mock objects can be injected with either this package's own stub() function or a similar with_mock() facility present in the testthat package.
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2025-03-25 |
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r-matrixstats
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public |
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
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2025-03-25 |
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r-speedglm
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public |
Fitting linear models and generalized linear models to large data sets by updating algorithms.
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2025-03-25 |
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r-simcausal
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A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.
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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-rstackdeque
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public |
Provides fast, persistent (side-effect-free) stack, queue and deque (double-ended-queue) data structures. While deques include a superset of functionality provided by queues, in these implementations queues are more efficient in some specialized situations. See the documentation for rstack, rdeque, and rpqueue for details.
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2025-03-25 |
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r-future
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public |
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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public |
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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public |
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-screening
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public |
An R package for efficient variable screening for linear and generalized linear models.
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2025-03-25 |
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r-lobstr
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public |
Visualise R data structures with trees. A set of tools for inspecting and understanding complex R data structures. Inspired by `str()`.
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2025-03-25 |
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r-abind
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public |
Combine multidimensional arrays into a single array. This is a generalization of 'cbind' and 'rbind'. Works with vectors, matrices, and higher-dimensional arrays. Also provides functions 'adrop', 'asub', and 'afill' for manipulating, extracting and replacing data in arrays.
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2025-03-25 |
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r-arm
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public |
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
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2025-03-25 |
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r-tmle
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public |
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
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2025-03-25 |
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r-snow
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public |
Support for simple parallel computing in R.
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2025-03-25 |
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r-rhpcblasctl
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public |
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'ACML' and 'MKL'). And possible to control the number of threads in 'OpenMP'. Get a number of logical cores and physical cores if feasible.
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2025-03-25 |
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r-reader
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A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
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2025-03-25 |
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r-rann
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Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L2 (Euclidean) metric. Please see package 'RANN.L1' for the same functionality using the L1 (Manhattan, taxicab) metric.
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2025-03-25 |
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r-proftools
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Tools for examining Rprof profile output.
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2025-03-25 |
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r-ncmisc
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A set of handy functions. Includes a versatile one line progress bar, one line function timer with detailed output, time delay function, text histogram, object preview, CRAN package search, simpler package installer, Linux command install check, a flexible Mode function, top function, simulation of correlated data, and more.
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2025-03-25 |
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r-dosnow
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Provides a parallel backend for the %dopar% function using the snow package of Tierney, Rossini, Li, and Sevcikova.
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2025-03-25 |
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r-ck37r
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Toolkit for statistical, machine learning, and targeted learning analyses. Functionality includes loading & auto-installing packages, standardizing datasets, creating missingness indicators, imputing missing values, creating multicore or multinode clusters, automatic SLURM integration, enhancing SuperLearner and TMLE with automatic parallelization, and many other SuperLearner analysis & plotting enhancements.
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2025-03-25 |
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r-nnls
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An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non-positive constraints.
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2025-03-25 |
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r-cvauc
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This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively. One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods. The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
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2025-03-25 |
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r-superlearner
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Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
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2025-03-25 |
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tensorly
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public |
No Summary
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2025-03-25 |
