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r-bucss
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Implements a method of correcting for publication bias and uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).
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2023-06-16 |
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r-bs4dash
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Make 'Bootstrap 4' dashboards. Use the full power of 'AdminLTE3', a dashboard template built on top of 'Bootstrap 4' <https://github.com/almasaeed2010/AdminLTE/tree/v3-dev>.
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2023-06-16 |
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r-brobdingnag
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Handles very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. The package includes a vignette that gives a step-by-step introduction to using S4 methods.
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2023-06-16 |
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r-bracer
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Performs brace expansions on strings. Made popular by Unix shells, brace expansion allows users to quickly generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string. The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.
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2023-06-16 |
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r-bvls
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An R interface to the Stark-Parker implementation of an algorithm for bounded-variable least squares
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2023-06-16 |
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r-btm
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Biterm Topic Models find topics in collections of short texts. It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms. This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis which are word-document co-occurrence topic models. A biterm consists of two words co-occurring in the same short text window. This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.
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2023-06-16 |
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r-boxoffice
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Download daily box office information (how much each movie earned in theaters) using data from either Box Office Mojo (<http://www.boxofficemojo.com/>) or The Numbers (<http://www.the-numbers.com/>).
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2023-06-16 |
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r-bootres
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Bootstrapped correlation and response functions are widely used in dendrochronology to calibrate tree-ring proxy data against multiple instrumental time series of climatic variables. With this package, we provide functionality similar to DENDROCLIM2002 (Biondi and Waikul (2004) <10.1016/j.cageo.2003.11.004>). A full description with examples is given in Zang and Biondi (2013) <10.1016/j.dendro.2012.08.001>.
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2023-06-16 |
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r-bwgr
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Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, univariate and multivariate, with optional kernel term and sampling techniques.
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2023-06-16 |
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r-brunnermunzel
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Provides the functions for Brunner-Munzel test and permuted Brunner-Munzel test, which enable to use formula, matrix, and table as argument. These functions are based on Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U> and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>, and are written with FORTRAN.
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2023-06-16 |
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r-bwimage
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A computational tool to describe patterns in black and white images from natural structures. 'bwimage' implemented functions for exceptionally broad subject. For instance, 'bwimage' provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure.
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2023-06-16 |
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r-bunchr
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View and analyze data where bunching is expected. Estimate counter- factual distributions. For earnings data, estimate the compensated elasticity of earnings w.r.t. the net-of-tax rate.
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2023-06-16 |
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r-boottol
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Used to create bootstrap tolerance levels for the Kolmogorov-Smirnov (KS) statistic, the area under receiver operator characteristic curve (AUROC) statistic, and the Gini coefficient for each score cutoff. Also provides a bootstrap alternative to the Vasicek test.
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2023-06-16 |
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r-bootes
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Calculate robust measures of effect sizes using the bootstrap.
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2023-06-16 |
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r-boostr
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boostr provides a modular framework that return the focus of ensemble learning back to 'learning' (instead of programming).
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2023-06-16 |
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r-assertive.data.us
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A set of predicates and assertions for checking the properties of US-specific complex data types. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly.
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2023-06-16 |
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r-bzinb
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Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details.
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2023-06-16 |
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r-bvarsv
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R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
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2023-06-16 |
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r-boolnet
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Provides methods to reconstruct and generate synchronous, asynchronous, probabilistic and temporal Boolean networks, and to analyze and visualize attractors in Boolean networks.
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2023-06-16 |
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r-bnsl
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From a given data frame, this package learns its Bayesian network structure based on a selected score.
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2023-06-16 |
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r-brm
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Fits novel models for the conditional relative risk, risk difference and odds ratio.
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2023-06-16 |
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r-breakaway
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Species richness estimation is an important problem in biodiversity analysis. This package provides methods for total species richness estimation (observed plus unobserved) and a method for modelling total diversity with covariates. breakaway() estimates total (observed plus unobserved) species richness. Microbial diversity datasets are characterized by a large number of rare species and a small number of highly abundant species. The class of models implemented by breakaway() is flexible enough to model both these features. breakaway_nof1() implements a similar procedure however does not require a singleton count. betta() provides a method for modelling total diversity with covariates in a way that accounts for its estimated nature and thus accounts for unobserved taxa, and betta_random() permits random effects modelling.
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2023-06-16 |
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r-bolstad
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A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.
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2023-06-16 |
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r-boardgames
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Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.
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2023-06-16 |
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r-buysetest
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Implementation of the Generalized Pairwise Comparisons (GPC). GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints. The net benefit and win ratio statistics can then be estimated and corresponding confidence intervals and p-values can be estimated using resampling methods or the asymptotic U-statistic theory. The software enables the use of thresholds of minimal importance difference, stratification, and corrections to deal with right-censored endpoints or missing values.
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2023-06-16 |
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r-buddle
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Statistical classification has been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades. It is a veritable technique which was originally designed for the classification, and hence, the EzDL package can provide sublime solutions to various challenging classification problems encountered in the clinical trials. The EzDL package is based on the back-propagation algorithm which performs a multi-layer feed-forward neural network. This package contains two functions: Buddle_Main() and Buddle_Predict(). Buddle_Main() builds a feed-forward neural network model and trains the model. Buddle_Predict() provokes the trained model which is the output of Buddle_Main(), classifies given data, and make a final prediction for the data.
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2023-06-16 |
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r-bondvaluation
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Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond.
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2023-06-16 |
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r-brotli
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A lossless compressed data format that uses a combination of the LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip) but offers more dense compression.
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2023-06-16 |
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r-branching
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Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes.
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2023-06-16 |
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r-c2c
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Compare two classifications or clustering solutions that may or may not have the same number of classes, and that might have hard or soft (fuzzy, probabilistic) membership. Calculate various metrics to assess how the clusters compare to each other. The calculations are simple, but provide a handy tool for users unfamiliar with matrix multiplication. This package is not geared towards traditional accuracy assessment for classification/ mapping applications - the motivating use case is for comparing a probabilistic clustering solution to a set of reference or existing class labels that could have any number of classes (that is, without having to degrade the probabilistic clustering to hard classes).
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2023-06-16 |
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r-busdater
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Get a current financial year, start of current month, End of current month, start of financial year and end of it. Allow for offset from the date.
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2023-06-16 |
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r-bursts
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An implementation of Jon Kleinberg's burst detection algorithm. Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.
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2023-06-16 |
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r-bundesligr
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All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016.
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2023-06-16 |
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r-bytescircle
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Shows statistics about bytes contained in a file as a circle graph of deviations from mean in sigma increments. The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc).
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2023-06-16 |
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r-binomsamsize
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A suite of functions to compute confidence intervals and necessary sample sizes for the parameter p of the Bernoulli B(p) distribution under simple random sampling or under pooled sampling. Such computations are e.g. of interest when investigating the incidence or prevalence in populations. The package contains functions to compute coverage probabilities and coverage coefficients of the provided confidence intervals procedures. Sample size calculations are based on expected length.
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2023-06-16 |
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r-bayhap
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The package BayHap performs simultaneous estimation of uncertain haplotype frequencies and association with haplotypes based on generalized linear models for quantitative, binary and survival traits. Bayesian statistics and Markov Chain Monte Carlo techniques are the theoretical framework for the methods of estimation included in this package. Prior values for model parameters can be included by the user. Convergence diagnostics and statistical and graphical analysis of the sampling output can be also carried out.
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2023-06-16 |
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r-brlrmr
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Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068> to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) <https://github.com/arnabkrmaity/brlrmr> to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068>.
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2023-06-16 |
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r-bmk
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MCMC diagnostic package that contains tools to diagnose convergence as well as to evaluate sensitivity studies, Includes summary functions which output mean, median, 95percentCI, Gelman & Rubin diagnostics and the Hellinger distance based diagnostics, Also contains functions to determine when an MCMC chain has converged via Hellinger distance, A function is also provided to compare outputs from identically dimensioned chains for determining sensitivy to prior distribution assumptions
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2023-06-16 |
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r-bivgeo
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Computes the joint probability mass function (pmf), the joint cumulative function (cdf), the joint survival function (sf), the correlation coefficient, the covariance, the cross-factorial moment and generate random deviates for the Basu-Dhar bivariate geometric distribution as well the joint probability mass, cumulative and survival function assuming the presence of a cure fraction given by the standard bivariate mixture cure fraction model. The package also computes the estimators based on the method of moments.
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2023-06-16 |
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r-birk
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Collection of tools to make R more convenient. Includes tools to summarize data using statistics not available with base R and manipulate objects for analyses.
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2023-06-16 |
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r-bionetdata
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Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
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2023-06-16 |
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r-biodem
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The Biodem package provides a number of functions for Biodemographic analysis.
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2023-06-16 |
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r-binb
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A collection of 'LaTeX' styles using 'Beamer' customization for pdf-based presentation slides in 'RMarkdown'. At present it contains 'RMarkdown' adaptations of the LaTeX themes 'Metropolis' (formerly 'mtheme') theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the 'IQSS' by Ista Zahn (which is included here), and the 'Monash' theme by Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers 'Fira', and 'IQSS' requires 'Libertinus'.
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2023-06-16 |
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r-binostics
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Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot. They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.
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2023-06-16 |
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r-bnptsclust
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Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014).
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2023-06-16 |
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r-bmp
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Reads Windows BMP format images. Currently limited to 8 bit greyscale images and 24,32 bit (A)RGB images. Pure R implementation without external dependencies.
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2023-06-16 |
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r-bmass
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Multivariate tool for analyzing genome-wide association study results in the form of univariate summary statistics. The goal of 'bmass' is to comprehensively test all possible multivariate models given the phenotypes and datasets provided. Multivariate models are determined by assigning each phenotype to being either Unassociated (U), Directly associated (D) or Indirectly associated (I) with the genetic variant of interest. Test results for each model are presented in the form of Bayes factors, thereby allowing direct comparisons between models. The underlying framework implemented here is based on the modeling developed in "A Unified Framework for Association Analysis with Multiple Related Phenotypes", M. Stephens (2013) <doi:10.1371/journal.pone.0065245>.
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2023-06-16 |
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r-blin
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Estimate influence networks from longitudinal bipartite relational data, where the longitudinal relations are continuous. The outputs are estimates of weighted influence networks among each actor type in the data set. The generative model is the Bipartite Longitudinal Influence Network (BLIN) model, a linear autoregressive model for these type of data. The supporting paper is ``Inferring Influence Networks from Longitudinal Bipartite Relational Data'', which is in preparation by the same authors. The model may be estimated using maximum likelihood methods and Bayesian methods. For more detail on methods, see Marrs et. al. <arXiv:1809.03439>.
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2023-06-16 |
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r-blendstat
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Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.
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2023-06-16 |
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r-blendedlink
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A new link function that equals one specified link function up to a cutover then a linear rescaling of another specified link function. For use in glm() or glm2(). The intended use is in binary regression, in which case the first link should be set to "log" and the second to "logit". This ensures that fitted probabilities are between 0 and 1 and that exponentiated coefficients can be interpreted as relative risks for probabilities up to the cutoff.
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2023-06-16 |
