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r-fauxpas
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HTTP error helpers. Methods included for general purpose HTTP error handling, as well as individual methods for every HTTP status code, both via status code numbers as well as their descriptive names. Supports ability to adjust behavior to stop, message or warning. Includes ability to use custom whisker template to have any configuration of status code, short description, and verbose message. Currently supports integration with 'crul', 'curl', and 'httr'.
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2024-01-16 |
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r-favnums
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A dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos (http://pages.bloomsbury.com/favouritenumber).
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2024-01-16 |
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r-faux
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Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://debruine.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.
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2024-01-16 |
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r-fastai
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The 'fastai' <https://docs.fast.ai/index.html> library simplifies training fast and accurate neural networks using modern best practices. It is based on research in to deep learning best practices undertaken at 'fast.ai', including 'out of the box' support for vision, text, tabular, audio, time series, and collaborative filtering models.
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2024-01-16 |
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r-fastverse
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Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. The core 'fastverse' consists of 4 packages: 'data.table', 'collapse', 'kit' and 'magrittr', that jointly only depend on 'Rcpp'. The 'fastverse' can be freely and permanently extended with additional packages, both globally or for individual projects. Separate package verses can also be created. Fast packages for many common tasks such as time series, dates and times, strings, spatial data, statistics, data serialization, larger-than-memory processing, and compilation of R code are listed in the README file: <https://github.com/fastverse/fastverse#suggested-extensions>.
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2024-01-16 |
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r-fastdummies
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Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().
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2024-01-16 |
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r-fastnaivebayes
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This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
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2024-01-16 |
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r-fassets
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A collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view.
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2024-01-16 |
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r-fastimputation
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TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class 'FastImputationPatterns'. FastImputation() function uses this 'FastImputationPatterns' object to impute (make a good guess at) missing data in a single line or a whole data frame of data. This approximates the process used by 'Amelia' <https://gking.harvard.edu/amelia> but is much faster when filling in values for a single line of data.
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2024-01-16 |
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r-fastgraph
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Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
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2024-01-16 |
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r-factoshiny
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Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
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2024-01-16 |
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r-fasjem
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This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.
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2024-01-16 |
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r-fasta
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A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.
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2024-01-16 |
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r-faseg
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It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.
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2024-01-16 |
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r-faraway
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Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).
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2024-01-16 |
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r-fanplot
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Visualise sequential distributions using a range of plotting styles. Sequential distribution data can be input as either simulations or values corresponding to percentiles over time. Plots are added to existing graphic devices using the fan function. Users can choose from four different styles, including fan chart type plots, where a set of coloured polygon, with shadings corresponding to the percentile values are layered to represent different uncertainty levels. Full details in R Journal article; Abel (2015) <doi:10.32614/RJ-2015-002>.
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2024-01-16 |
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r-fancycut
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Provides the function fancycut() which is like cut() except you can mix left open and right open intervals with point values, intervals that are closed on both ends and intervals that are open on both ends.
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2024-01-16 |
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r-factoextra
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Provides some easy-to-use functions to extract and visualize the output of multivariate data analyses, including 'PCA' (Principal Component Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple Factor Analysis) functions from different R packages. It contains also functions for simplifying some clustering analysis steps and provides 'ggplot2' - based elegant data visualization.
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2024-01-16 |
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r-fancova
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A collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. Testing the equality or parallelism of nonparametric curves or surfaces is equivalent to analysis of variance (ANOVA) or analysis of covariance (ANCOVA) for one-sample functional data. Three different testing methods are available in the package, including one based on L-2 distance, one based on an ANOVA statistic, and one based on variance estimators.
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2024-01-16 |
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r-fam.recrisk
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Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.
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2024-01-16 |
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r-fail
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More comfortable interface to work with R data or source files in a key-value fashion.
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2024-01-16 |
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r-factoinvestigate
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Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
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2024-01-16 |
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r-fahrmeir
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Data and functions for the book "Multivariate Statistical Modelling Based on Generalized Linear Models", first edition, by Ludwig Fahrmeir and Gerhard Tutz. Useful when using the book.
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2024-01-16 |
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r-fadist
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Probability distributions that are sometimes useful in hydrology.
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2024-01-16 |
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r-factory
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Function factories are functions that make functions. They can be confusing to construct. Straightforward techniques can produce functions that are fragile or hard to understand. While more robust techniques exist to construct function factories, those techniques can be confusing. This package is designed to make it easier to construct function factories.
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2024-01-16 |
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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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2024-01-16 |
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r-factorsr
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It identifies the factors significantly related to species richness, and their relative contribution, using multiple regressions and support vector machine models. It uses an output file of 'ModestR' with data of richness of the species and environmental variables in a cell size defined by the user. The residuals of the support vector machine model are shown on a map. Negative residuals may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors.
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2024-01-16 |
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r-factorial2x2
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Used for the design and analysis of a 2x2 factorial trial for a time-to-event endpoint. It performs power calculations and significance testing as well as providing estimates of the relevant hazard ratios and the corresponding 95% confidence intervals. Important reference papers include Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609> Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507> Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020) <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.
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2024-01-16 |
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r-fabletools
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Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
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2024-01-16 |
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r-extrafont
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Tools to using fonts other than the standard PostScript fonts. This package makes it easy to use system TrueType fonts and with PDF or PostScript output files, and with bitmap output files in Windows. extrafont can also be used with fonts packaged specifically to be used with, such as the fontcm package, which has Computer Modern PostScript fonts with math symbols.
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2024-01-16 |
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r-fabricatr
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Helps you imagine your data before you collect it. Hierarchical data structures and correlated data can be easily simulated, either from random number generators or by resampling from existing data sources. This package is faster with 'data.table' and 'mvnfast' installed.
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2024-01-16 |
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r-fabci
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Frequentist assisted by Bayes (FAB) confidence interval construction. See 'Adaptive multigroup confidence intervals with constant coverage' by Yu and Hoff <DOI:10.1093/biomet/asy009> and 'Exact adaptive confidence intervals for linear regression coefficients' by Hoff and Yu <DOI:10.1214/18-EJS1517>.
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2024-01-16 |
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r-extraoperators
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Speed up common tasks, particularly logical or relational comparisons and routine follow up tasks such as finding the indices and subsetting. Inspired by mathematics, where something like: 3 < x < 6 is a standard, elegant and clear way to assert that x is both greater than 3 and less than 6 (see for example <https://en.wikipedia.org/wiki/Relational_operator>), a chaining operator is implemented. The chaining operator, %c%, allows multiple relational operations to be used in quotes on the right hand side for the same object, on the left hand side. The %e% operator allows something like set-builder notation (see for example <https://en.wikipedia.org/wiki/Set-builder_notation>) to be used on the right hand side. All operators have built in prefixes defined for all, subset, and which to reduce the amount of code needed for common tasks, such as return those values that are true.
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2024-01-16 |
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r-ezknitr
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An extension of 'knitr' that adds flexibility in several ways. One common source of frustration with 'knitr' is that it assumes the directory where the source file lives should be the working directory, which is often not true. 'ezknitr' addresses this problem by giving you complete control over where all the inputs and outputs are, and adds several other convenient features to make rendering markdown/HTML documents easier.
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2024-01-16 |
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r-extremogram
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Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.
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2024-01-16 |
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r-extremefit
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Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities.
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2024-01-16 |
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r-extras
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Functions to 'numericise' 'R' objects (coerce to numeric objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and calculate deviance residuals as well as 'R' translations of some 'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.
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2024-01-16 |
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r-extremebounds
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An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
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2024-01-16 |
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r-expsmooth
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Data sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008).
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2024-01-16 |
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r-extrafontdb
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Package for holding the database for the extrafont package
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2024-01-16 |
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r-export
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Easily export 'R' graphs and statistical output to 'Microsoft Office' / 'LibreOffice', 'Latex' and 'HTML' Documents, using sensible defaults that result in publication-quality output with simple, straightforward commands. Output to 'Microsoft Office' is in editable 'DrawingML' vector format for graphs, and can use corporate template documents for styling. This enables the production of standardized reports and also allows for manual tidy-up of the layout of 'R' graphs in 'Powerpoint' before final publication. Export of graphs is flexible, and functions enable the currently showing R graph or the currently showing 'R' stats object to be exported, but also allow the graphical or tabular output to be passed as objects. The package relies on package 'officer' for export to 'Office' documents,and output files are also fully compatible with 'LibreOffice'. Base 'R', 'ggplot2' and 'lattice' plots are supported, as well as a wide variety of 'R' stats objects, via wrappers to xtable(), broom::tidy() and stargazer(), including aov(), lm(), glm(), lme(), glmnet() and coxph() as well as matrices and data frames and many more...
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2024-01-16 |
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r-extmallows
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For multiple full/partial ranking lists, R package 'ExtMallows' can (1) detect whether the input ranking lists are over-correlated, and (2) use the Mallows model or extended Mallows model to integrate the ranking lists, and (3) use hierarchical extended Mallows model for rank integration if there are groups of over-correlated ranking lists.
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2024-01-16 |
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r-extlasso
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Estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. This package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. This package also contains functions for cross validation to select a suitable lambda value given the data. Also provides a function for estimation in fused lasso penalized linear regression. For more details, see Mandal, B. N.(2014). Computational methods for L1 penalized GLM model fitting, unpublished report submitted to Macquarie University, NSW, Australia.
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2024-01-16 |
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r-exteriormatch
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If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups. The exterior match is used to compare the two control groups.
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2024-01-16 |
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r-exposition
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A variety of descriptive multivariate analyses with the singular value decomposition, such as principal components analysis, correspondence analysis, and multidimensional scaling. See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
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2024-01-16 |
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r-exrq
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Estimation for high conditional quantiles based on quantile regression.
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2024-01-16 |
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r-expss
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Package computes and displays tables with support for 'SPSS'-style labels, multiple and nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in 'knitr', 'Shiny', '*.xlsx' files, R and 'Jupyter' notebooks. Methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package brings popular data transformation functions from 'SPSS' Statistics and 'Excel': 'RECODE', 'COUNT', 'COUNTIF', 'VLOOKUP' and etc. These functions are very useful for data processing in marketing research surveys. Package intended to help people to move data processing from 'Excel' and 'SPSS' to R.
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2024-01-16 |
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r-exprep
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Allows to calculate the probabilities of occurrences of an event in a great number of repetitions of Bernoulli experiment, through the application of the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. Gives the possibility to show the results graphically and analytically, and to compare the results obtained by the application of the above theorems with those calculated by the direct application of the Binomial formula. Is basically useful for educational purposes.
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2024-01-16 |
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r-explore
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Interactive data exploration with one line of code, automated reporting or use an easy to remember set of tidy functions for low code exploratory data analysis.
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2024-01-16 |
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r-exifr
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Reads EXIF data using ExifTool <https://exiftool.org> and returns results as a data frame. ExifTool is a platform-independent Perl library plus a command-line application for reading, writing and editing meta information in a wide variety of files. ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR, FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo, Sigma/Foveon and Sony.
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2024-01-16 |
