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r-bbmm
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The model provides an empirical estimate of a movement path using discrete location data obtained at relatively short time intervals.
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2023-06-16 |
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r-aws.iam
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A simple client for the Amazon Web Services ('AWS') Identity and Access Management ('IAM') 'API' <https://aws.amazon.com/iam/>.
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2023-06-16 |
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r-awr.kms
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Encrypt plain text and 'decrypt' cipher text using encryption keys hosted at Amazon Web Services ('AWS') Key Management Service ('KMS'), on which see <https://aws.amazon.com/kms> for more information.
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2023-06-16 |
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r-beyondbenford
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Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).
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2023-06-16 |
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r-betabit
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Three games: proton, frequon and regression. Each one is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. In proton you have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages. This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge? In regression you will test your modeling skills in a series of eight sub-tasks. Try only if ANOVA is your close friend. It's a part of Beta and Bit project. You will find more about the Beta and Bit project at <http://betabit.wiki>.
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2023-06-16 |
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r-bbmle
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Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.
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2023-06-16 |
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r-aws.s3
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A simple client package for the Amazon Web Services ('AWS') Simple Storage Service ('S3') 'REST' 'API' <https://aws.amazon.com/s3/>.
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2023-06-16 |
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r-aws.comprehend
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public |
Client for 'AWS Comprehend' <https://aws.amazon.com/comprehend>, a cloud natural language processing service that can perform a number of quantitative text analyses, including language detection, sentiment analysis, and feature extraction.
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2023-06-16 |
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r-ber
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The functions in this package remove batch effects from microarrary normalized data. The expression levels of the genes are represented in a matrix where rows correspond to independent samples and columns to genes (variables). The batches are represented by categorical variables (objects of class factor). When further covariates of interest are available they can be used to remove efficiently the batch effects and adjust the data.
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2023-06-16 |
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r-bentcablear
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Included are two main interfaces for fitting and diagnosing bent-cable regressions for autoregressive time-series data or independent data (time series or otherwise): 'bentcable.ar()' and 'bentcable.dev.plot()'. Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrects a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improves robustness of the error-message producing mechanism. It is the author's intention to distribute any future updates via GitHub.
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2023-06-16 |
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r-beepr
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The main function of this package is beep(), with the purpose to make it easy to play notification sounds on whatever platform you are on. It is intended to be useful, for example, if you are running a long analysis in the background and want to know when it is ready.
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2023-06-16 |
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r-bbefkr
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Estimating optimal bandwidths for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals simultaneously in a scalar-on-function regression. As a by-product of Markov chain Monte Carlo, the optimal choice of semi-metric is selected based on largest marginal likelihood.
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2023-06-16 |
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r-bcdating
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Tools for Dating Business Cycles using Harding-Pagan (Quarterly Bry-Boschan) method and various plotting features.
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2023-06-16 |
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r-bcc1997
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Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.
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2023-06-16 |
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r-bayhaz
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A suite of R functions for Bayesian estimation of smooth hazard rates via Compound Poisson Process (CPP) and Bayesian Penalized Spline (BPS) priors.
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2023-06-16 |
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r-assertive.files
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A set of predicates and assertions for checking the properties of files and connections. 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-bezier
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The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.
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2023-06-16 |
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r-betalink
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Measures of beta-diversity in networks, and easy visualization of why two networks are different.
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2023-06-16 |
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r-betafam
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To detecting rare variants for quantitative traits using nuclear families, the linear combination methods are proposed using the estimated regression coefficients from the multiple regression and regularized regression as the weights.
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2023-06-16 |
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r-bender
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R client for Bender Hyperparameters optimizer : <https://bender.dreem.com> The R client allows you to communicate with the Bender API and therefore submit some new trials within your R script itself.
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2023-06-16 |
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r-bayespiecehazselect
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Fits a piecewise exponential hazard to survival data using a Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive formulation for the spatial dependency in the hazard rates for each piece. This function uses Metropolis- Hastings-Green MCMC to allow the number of split points to vary and also uses Stochastic Search Variable Selection to determine what covariates drive the risk of the event. This function outputs trace plots depicting the number of split points in the hazard and the number of variables included in the hazard. The function saves all posterior quantities to the desired path.
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2023-06-16 |
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r-benfordtests
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Several specialized statistical tests and support functions for determining if numerical data could conform to Benford's law.
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2023-06-16 |
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r-bclust
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Builds a dendrogram using log posterior as a natural distance defined by the model and meanwhile waits the clustering variables. It is also capable to computing equivalent Bayesian discrimination probabilities. The adopted method suites small sample large dimension setting. The model parameter estimation maybe difficult, depending on data structure and the chosen distribution family.
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2023-06-16 |
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r-bayesxsrc
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BayesX performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the BayesX command line tool for easy installation. A convenient R interface is provided in package R2BayesX.
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2023-06-16 |
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r-bhsbvar
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Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.
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2023-06-16 |
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r-betategarch
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Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).
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2023-06-16 |
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r-bcf
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Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2017) <arXiv:1706.09523> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.
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2023-06-16 |
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r-bcbcsf
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Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.
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2023-06-16 |
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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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2023-06-16 |
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r-bayesimages
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Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details.
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2023-06-16 |
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r-bfp
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Implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.
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2023-06-16 |
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r-bcrypt
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Bindings to the 'blowfish' password hashing algorithm derived from the 'OpenBSD' implementation.
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2023-06-16 |
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r-bayessae
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Provides a variety of methods from Rao (2003, ISBN:0-471-41374-7) and some other research articles to deal with several specific small area area- level models in Bayesian framework. Models provided range from the basic Fay-Herriot model to its improvement such as You-Chapman models, unmatched models, spatial models and so on. Different types of priors for specific parameters could be chosen to obtain MCMC posterior draws. The main sampling function is written in C with GSL lab so as to facilitate the computation. Model internal checking and model comparison criteria are also involved.
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2023-06-16 |
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r-bfa
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Provides model fitting for several Bayesian factor models including Gaussian, ordinal probit, mixed and semiparametric Gaussian copula factor models under a range of priors.
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2023-06-16 |
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r-bcv
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Methods for choosing the rank of an SVD approximation via cross validation. The package provides both Gabriel-style "block" holdouts and Wold-style "speckled" holdouts. It also includes an implementation of the SVDImpute algorithm. For more information about Bi-cross-validation, see Owen & Perry's 2009 AoAS article (at http://arxiv.org/abs/0908.2062) and Perry's 2009 PhD thesis (at http://arxiv.org/abs/0909.3052).
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2023-06-16 |
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r-bayestree
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This is an implementation of BART:Bayesian Additive Regression Trees, by Chipman, George, McCulloch (2010).
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2023-06-16 |
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r-bayesloglin
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The function MC3() searches for log-linear models with the highest posterior probability. The function gibbsSampler() is a blocked Gibbs sampler for sampling from the posterior distribution of the log-linear parameters. The functions findPostMean() and findPostCov() compute the posterior mean and covariance matrix for decomposable models which, for these models, is available in closed form.
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2023-06-16 |
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r-benchr
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Provides infrastructure to accurately measure and compare the execution time of R expressions.
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2023-06-16 |
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r-bdgraph
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Statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi and Wit (2019) <doi:10.18637/jss.v089.i03>.
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2023-06-16 |
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r-aws
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We provide a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation Approach to adaptive smoothing as described in "J. Polzehl and V. Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1>", "J. Polzehl and V. Spokoiny (2004) <DOI:10.20347/WIAS.PREPRINT.998>" and "J. Polzehl, K. Papafitsoros, K. Tabelow (2018) <DOI:10.20347/WIAS.PREPRINT.2520>", the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.
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2023-06-16 |
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r-biasedurn
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Statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution (also called extended hypergeometric distribution). See vignette("UrnTheory") for explanation of these distributions.
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2023-06-16 |
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r-bayesvarsel
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Conceived to calculate Bayes factors in linear models and then to provide a formal Bayesian answer to testing and variable selection problems. From a theoretical side, the emphasis in this package is placed on the prior distributions and it allows a wide range of them: Jeffreys (1961); Zellner and Siow(1980)<DOI:10.1007/bf02888369>; Zellner and Siow(1984); Zellner (1986)<DOI:10.2307/2233941>; Fernandez et al. (2001)<DOI:10.1016/s0304-4076(00)00076-2>; Liang et al. (2008)<DOI:10.1198/016214507000001337> and Bayarri et al. (2012)<DOI:10.1214/12-aos1013>. The interaction with the package is through a friendly interface that syntactically mimics the well-known lm() command of R. The resulting objects can be easily explored providing the user very valuable information (like marginal, joint and conditional inclusion probabilities of potential variables; the highest posterior probability model, HPM; the median probability model, MPM) about the structure of the true -data generating- model. Additionally, this package incorporates abilities to handle problems with a large number of potential explanatory variables through parallel and heuristic versions of the main commands, Garcia-Donato and Martinez-Beneito (2013)<DOI:10.1080/01621459.2012.742443>.
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2023-06-16 |
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r-adimpro
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Implements tools for manipulation of digital images and the Propagation Separation approach by Polzehl and Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1> for smoothing digital images, see Polzehl and Tabelow (2007) <DOI:10.18637/jss.v019.i01>.
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2023-06-16 |
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r-bess
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An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2017) <arXiv:1709.06254>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
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2023-06-16 |
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r-bayesqr
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Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.
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2023-06-16 |
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r-bevimed
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A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.
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2023-06-16 |
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r-belg
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Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>). It also extend the original idea by allowing calculations on data with missing values.
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2023-06-16 |
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r-bayesmixsurv
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Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components in the hazard function.
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2023-06-16 |
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r-bda
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Functions for density estimation based on grouped data, and next-generation gene expression data.
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2023-06-16 |
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r-bcpa
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The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408.
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2023-06-16 |
