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r-bnormnlr
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Implementation of Bayesian estimation in normal heteroscedastic nonlinear regression Models following Cepeda-Cuervo, (2001).
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2023-06-16 |
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r-bndatagenerator
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Data generator based on Bayesian network model
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2023-06-16 |
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r-allan
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Automated fitting of linear regression models and a stepwise routine
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2023-06-16 |
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r-bootspecdens
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Bootstrap for testing the hypothesis that the spectral densities of a number m, m>=2, not necessarily independent time series are equal
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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-bpkde
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Nonparametric multivariate kernel density \ estimation using a back-projected kernel.
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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-bisect
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An implementation of Bisect, a method for inferring cell type composition of samples based on methylation sequencing data (Whole Genome Bisulfite Sequencing and Reduced Representation Sequencing). The method is specifically tailored for sequencing data, and therefore works better than methods developed for methylation arrays. It contains a supervised mode that requires a reference (the methylation probabilities in the pure cell types), and a semi-supervised mode, that requires cell counts for a subset of the samples, but does not require a reference.
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2023-06-16 |
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r-blockmessage
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Creates strings that show a text message in 8 by 8 block letters
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2023-06-16 |
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r-bigml
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The 'bigml' package contains bindings for the BigML API. The package includes methods that provide straightforward access to basic API functionality, as well as methods that accommodate idiomatic R data types and concepts.
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2023-06-16 |
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r-biclique
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A tool for enumerating maximal complete bipartite graphs. The input should be a edge list file or a binary matrix file. The output are maximal complete bipartite graphs. Algorithms used can be found in this paper Y. Lu et al. BMC Res Notes 13, 88 (2020) <doi:10.1186/s13104-020-04955-0>.
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2023-06-16 |
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r-biom.utils
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Provides utilities to facilitate import, export and computation with the BIOM (Biological Observation Matrix) format (http://biom-format.org).
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2023-06-16 |
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r-bivarripower
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Implements sample size calculations for bivariate random intercept regression model that are described in Comulada and Weiss (2010)
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2023-06-16 |
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r-binomlogit
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The R package contains different MCMC schemes to estimate the regression coefficients of a binomial (or binary) logit model within a Bayesian framework: a data-augmented independence MH-sampler, an auxiliary mixture sampler and a hybrid auxiliary mixture (HAM) sampler. All sampling procedures are based on algorithms using data augmentation, where the regression coefficients are estimated by rewriting the logit model as a latent variable model called difference random utility model (dRUM).
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2023-06-16 |
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r-binomialcftp
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Binomial random numbers are generated via the perfect sampling algorithm. At each iteration dual markov chains are generated and coalescence is checked. In case coalescence occurs, the resulting number is outputted. In case not, then the algorithm is restarted from T(t)=2*T(t) until coalescence occurs.
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2023-06-16 |
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r-bnpmr
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Implements the Bayesian nonparametric monotonic regression method described in Bornkamp & Ickstadt (2009), Biometrics, 65, 198-205.
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2023-06-16 |
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r-bnn
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Perform Bayesian variable selection for high-dimensional nonlinear systems and also can be used to test nonlinearity for a general regression problem. The computation can be accelerated using multiple CPUs. You can refer to <doi:10.1080/01621459.2017.1409122> for more detail.
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2023-06-16 |
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r-bpeaks
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bPeaks is a simple approach to identify transcription factor binding sites from ChIP-seq data. Our general philosophy is to provide an easy-to-use tool, well-adapted for small eukaryotic genomes (< 20 Mb). bPeaks uses a combination of 4 cutoffs (T1, T2, T3 and T4) to mimic "good peak" properties as described by biologists who visually inspect the ChIP-seq data on a genome browser. For yeast genomes, bPeaks calculates the proportion of peaks that fall in promoter sequences. These peaks are good candidates as transcription factor binding sites.
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2023-06-16 |
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r-bmrv
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Provides two Bayesian models for detecting the association between rare genetic variants and a trait that can be continuous, ordinal or binary. Bayesian latent variable collapsing model (BLVCM) detects interaction effect and is dedicated to twin design while it can also be applied to independent samples. Hierarchical Bayesian multiple regression model (HBMR) incorporates genotype uncertainty information and can be applied to either independent or family samples. Furthermore, it deals with continuous, binary and ordinal traits.
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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-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-blockfest
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An R implementation of an extension of the 'BayeScan' software (Foll, 2008) <DOI:10.1534/genetics.108.092221> for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.
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2023-06-16 |
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r-blsapi
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Allows users to request data for one or multiple series through the U.S. Bureau of Labor Statistics API. Users provide parameters as specified in <https://www.bls.gov/developers/api_signature.htm> and the function returns a JSON string.
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2023-06-16 |
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r-bitrugs
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MCMC methods to estimate transmission dynamics and infection routes in hospitals using genomic sampling data.
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2023-06-16 |
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r-aws.cloudtrail
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A simple client package for the Amazon Web Services ('AWS') 'CloudTrail' 'API' <https://aws.amazon.com/cloudtrail/>.
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2023-06-16 |
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r-bimixt
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Estimates non-Gaussian mixture models of case-control data. The four types of models supported are binormal, two component constrained, two component unconstrained, and four component. The most general model is the four component model, under which both cases and controls are distributed according to a mixture of two unimodal distributions. In the four component model, the two component distributions of the control mixture may be distinct from the two components of the case mixture distribution. In the two component unconstrained model, the components of the control and case mixtures are the same; however the mixture probabilities may differ for cases and controls. In the two component constrained model, all controls are distributed according to one of the two components while cases follow a mixture distribution of the two components. In the binormal model, cases and controls are distributed according to distinct unimodal distributions. These models assume that Box-Cox transformed case and control data with a common lambda parameter are distributed according to Gaussian mixture distributions. Model parameters are estimated using the expectation-maximization (EM) algorithm. Likelihood ratio test comparison of nested models can be performed using the lr.test function. AUC and PAUC values can be computed for the model-based and empirical ROC curves using the auc and pauc functions, respectively. The model-based and empirical ROC curves can be graphed using the roc.plot function. Finally, the model-based density estimates can be visualized by plotting a model object created with the bimixt.model function.
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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-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 |
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r-bimetallic
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A power calculator for Genome-wide association studies (GWAs) with combined gold (error-free) and silver (erroneous) phenotyping per McDavid A, Crane PK, Newton KM, Crosslin DR, et al. (2011)
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2023-06-16 |
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r-bkpc
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Bayesian kernel projection classifier is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. A Gibbs sampler is implemented to find the posterior distributions of the parameters.
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2023-06-16 |
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r-bitsqueezr
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Provides a implementation of floating-point quantization algorithms for use in precision-preserving compression, similar to the approach taken in the 'netCDF operators' (NCO) software package and described in Zender (2016) <doi:10.5194/gmd-2016-63>.
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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-binarylogic
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Provides the binary S3 class. The instance of binary is used to convert a decimal number (Base10) to a binary number (Base2). The Class provides some features e.G. shift(), rotate(), summary(). Based on logical vectors.
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2023-06-16 |
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r-bmix
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This is a bare-bones implementation of sampling algorithms for a variety of Bayesian stick-breaking (marginally DP) mixture models, including particle learning and Gibbs sampling for static DP mixtures, particle learning for dynamic BAR stick-breaking, and DP mixture regression. The software is designed to be easy to customize to suit different situations and for experimentation with stick-breaking models. Since particles are repeatedly copied, it is not an especially efficient implementation.
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2023-06-16 |
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r-bigintegeralgos
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Features the multiple polynomial quadratic sieve algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. Utilizes the C library GMP (GNU Multiple Precision Arithmetic) and classes created by Antoine Lucas et al. found in the 'gmp' package.
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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-bgsimd
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Implement an efficient block Gibbs sampler with incomplete data from a multinomial distribution taking values from the k categories 1,2,...,k, where data are assumed to miss at random and each missing datum belongs to one and only one of m distinct non-empty proper subsets A1, A2,..., Am of 1,2,...,k and the k categories are labelled such that only consecutive A's may overlap.
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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-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-beqi2
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Tool for analysing benthos data. It estimates several quality indices like the total abundance of species, species richness, Margalef's d, AZTI Marine Biotic Index (AMBI), and the BEQI-2 index. Furthermore, additional (optional) features are provided that enhance data preprocessing: (1) genus to species conversion, i.e.,taxa counts at the taxonomic genus level can optionally be converted to the species level and (2) pooling: small samples are combined to bigger samples with a standardized size to (a) meet the data requirements of the AMBI, (b) generate comparable species richness values and (c) give a higher benthos signal to noise ratio.
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2023-06-16 |
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r-bayloredpsych
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Functions and data used for Baylor University Educational Psychology Quantitative Courses
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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-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-bayesni
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A Bayesian testing procedure for noninferiority trials with binary endpoints. The prior is constructed based on Bernstein polynomials with options for both informative and non-informative prior. The critical value of the test statistic (Bayes factor) is determined by minimizing total weighted error (TWE) criteria
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2023-06-16 |
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r-bayesmams
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Calculating Bayesian sample sizes for multi-arm trials where several experimental treatments are compared to a common control, perhaps even at multiple stages.
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2023-06-16 |
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r-bcpmeta
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A Bayesian approach to detect mean shifts in AR(1) time series while accommodating metadata (if available). In addition, a linear trend component is allowed.
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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-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-bbmv
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Provides a set of functions to fit general macroevolutionary models for continuous traits evolving in adaptive landscapes of any shape. This package implements the Fokker-Planck-Kolmogorov model (FPK), in which the trait evolves under random diffusion but is also subject to a force that pulls it towards specific values - this force can be of any shape. FPK has a version in which hard reflective bounds exist at the extremes of the trait interval: this second model is called BBMV.
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2023-06-16 |
