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r-ez
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Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.
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2023-06-16 |
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r-mcmcpack
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This package contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return coda mcmc objects that can then be summarized using the coda package. MCMCpack also contains some useful utility functions, including some additional density functions and pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization.
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2023-06-16 |
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r-animation
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Provides functions for animations in statistics, covering topics in probability theory, mathematical statistics, multivariate statistics, nonparametric statistics, sampling survey, linear models, time series, computational statistics, data mining and machine learning. These functions may be helpful in teaching statistics and data analysis. Also provided in this package are a series of functions to save animations to various formats, e.g. Flash, GIF, HTML pages, PDF and videos (saveSWF(), saveGIF(), saveHTML(), saveLatex(), and saveVideo() respectively). PDF animations can be inserted into Sweave/knitr easily.
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2023-06-16 |
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r-gganimate
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gganimate wraps the animation package to create animated ggplot2 plots.
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2023-06-16 |
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r-lmertest
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Different kinds of tests for linear mixed effects models as implemented in 'lme4' package are provided. The tests comprise types I - III F tests for fixed effects, LR tests for random effects. The package also provides the calculation of population means for fixed factors with confidence intervals and corresponding plots. Finally the backward elimination of non-significant effects is implemented.
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2023-06-16 |
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r-ggally
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GGally is designed to be a helper to ggplot2. It contains templates for different plots to be combined into a plot matrix, a parallel coordinate plot function, as well as a function for making a network plot.
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2023-06-16 |
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r-rwiener
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This package provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions.
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2023-06-16 |
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r-pbapply
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A lightweight package that adds progress bar to vectorized R functions ('*apply'). The implementation can easily be added to functions, where showing the progress is useful for the user (e.g. bootstrap).
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2023-06-16 |
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r-bayesfactor
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A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.
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2023-06-16 |
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r-readxl
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Import excel files into R. Supports '.xls' via the embedded 'libxls' C library (http://sourceforge.net/projects/libxls/) and '.xlsx' via the embedded 'RapidXML' C++ library (http://rapidxml.sourceforge.net). Works on Windows, Mac and Linux without external dependencies.
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2023-06-16 |
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r-sm
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This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press.
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2023-06-16 |
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r-mi
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The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
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2023-06-16 |
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r-matrixstats
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Methods operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). There are also some vector-based methods, e.g. binMeans(), madDiff() and weightedMedians(). All methods have been optimized for speed and memory usage.
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2023-06-16 |
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r-modeest
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This package provides estimators of the mode of univariate unimodal data or univariate unimodal distributions
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2023-06-16 |
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r-rstanmulticore
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A cross-platform (Windows, Linux, and Mac) R package to parallelize RStan MCMC chains across multiple cores. The syntax is very simple: replace calls to stan(...) with pstan(...).
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2023-06-16 |
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r-inline
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Functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.
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2023-06-16 |
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r-projecttemplate
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ProjectTemplate provides functions to automatically build a directory structure for a new R project. Using this structure, ProjectTemplate automates data loading, preprocessing, library importing and unit testing.
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2023-06-16 |
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r-abind
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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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2023-06-16 |
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r-rstan
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rstan
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2023-06-16 |
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r-rpushbullet
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An R interface to the Pushbullet messaging service which provides fast and efficient notifications (and file transfer) between computers, phones and tablets. An account has to be registered at the site http://www.pushbullet.com site to obtain a (free) API key.
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2023-06-16 |
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r-arm
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R functions for processing 'lm', 'glm', 'svy.glm', 'merMod' and 'polr' outputs.
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2023-06-16 |
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r-loo
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We efficiently approximate leave-one-out cross-validation (LOO) using Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors, and for the comparison of predictive errors between two models. We also compute the widely applicable information criterion (WAIC).
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2023-06-16 |
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r-stanheaders
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The C++ header files of the Stan project are provided by this package, but it contains no R code, shared objects, vignettes, or function documentation. It is only useful for developers who want to utilize the LinkingTo directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo and (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, templated statistical and linear algebra functions that can handle the automatically differentiable scalar types (and doubles, ints, etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
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2023-06-16 |
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r-mice
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Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
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2023-06-16 |
