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r-burstmisc
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Script search, corner, genetic optimization, permutation tests, write expect test.
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2023-06-16 |
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r-bspmma
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The main functions carry out Gibbs' sampler routines for nonparametric and semiparametric Bayesian models for random effects meta-analysis.
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2023-06-16 |
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r-bsplus
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The Bootstrap framework lets you add some JavaScript functionality to your web site by adding attributes to your HTML tags - Bootstrap takes care of the JavaScript <https://getbootstrap.com/javascript>. If you are using R Markdown or Shiny, you can use these functions to create collapsible sections, accordion panels, modals, tooltips, popovers, and an accordion sidebar framework (not described at Bootstrap site).
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2023-06-16 |
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r-bretigea
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Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.
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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-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-binford
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Binford's hunter-gatherer data includes more than 200 variables coding aspects of hunter-gatherer subsistence, mobility, and social organization for 339 ethnographically documented groups of hunter-gatherers.
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2023-06-16 |
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r-bnstruct
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Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference.
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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-bmixture
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Provides statistical tools for Bayesian estimation for finite mixture of distributions, mainly mixture of Gamma, Normal and t-distributions. The package is implemented the recent improvements in Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.
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2023-06-16 |
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r-blockforest
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A random forest variant 'block forest' ('BlockForest') tailored to the prediction of binary, survival and continuous outcomes using block-structured covariate data, for example, clinical covariates plus measurements of a certain omics data type or multi-omics data, that is, data for which measurements of different types of omics data and/or clinical data for each patient exist. Examples of different omics data types include gene expression measurements, mutation data and copy number variation measurements. Block forest are presented in Hornung & Wright (2019). The package includes four other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), but performed worse than block forest in their comparison study based on 20 real multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') in applications. The other random forest variants can, however, be consulted for academic purposes, for example, in the context of further methodological developments. Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.
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2023-06-16 |
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r-bivrec
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Alternating recurrent event data arise frequently in biomedical and social sciences where 2 types of events such as hospital admissions and discharge occur alternatively over time. As such we implement a collection of non-parametric and semiparametric methods to analyze such data. The main functions are biv.rec.fit() and biv.rec.np(). Use biv.rec.fit() for estimation of covariate effects on the two alternating event gap times (xij and yij) using semiparametric methods. The method options are "Lee.et.al" and "Chang". Use biv.rec.np() for estimation of the joint cumulative distribution function (cdf) for the two alternating events gap times (xij and yij) as well as the marginal survival function for type I gap times (xij) and the conditional cdf of the type II gap times (yij) given an interval of type I gap times (xij) in a non-parametric fashion. The package also provides options to simulate and visualize the data and results of analysis.
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2023-06-16 |
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r-blmodel
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Posterior distribution in the Black-Litterman model is computed from a prior distribution given in the form of a time series of asset returns and a continuous distribution of views provided by the user as an external function.
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2023-06-16 |
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r-blandaltmanleh
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Bland-Altman Plots using either base graphics or ggplot2, augmented with confidence intervals, with detailed return values and a sunflowerplot option for data with ties.
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2023-06-16 |
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r-bivgeom
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Implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.
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2023-06-16 |
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r-bio.infer
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Imports benthic count data, reformats this data, and computes environmental inferences from this data.
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2023-06-16 |
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r-bimets
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Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, endogenous targeting and model renormalization.
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2023-06-16 |
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r-bmamevt
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Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models.
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2023-06-16 |
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r-bife
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Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with an asymptotic bias-correction proposed by Fernandez-Val (2009) <doi:10.1016/j.jeconom.2009.02.007>.
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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 Zhang et al. BMC Bioinformatics 2014 15:110 <doi:10.1186/1471-2105-15-110>.
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2023-06-16 |
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r-birtr
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R functions for "The Basics of Item Response Theory Using R" by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch(). For example, iccplot() plots an item characteristic curve under the two-parameter logistic model.
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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-burstfin
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A suite of functions for finance, including the estimation of variance matrices via a statistical factor model or Ledoit-Wolf shrinkage.
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2023-06-16 |
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r-bridgedist
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An implementation of the bridge distribution with logit-link in R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate bridge distribution was derived as the distribution of the random intercept that 'bridged' a marginal logistic regression and a conditional logistic regression. The conditional and marginal regression coefficients are a scalar multiple of each other. Such is not the case if the random intercept distribution was Gaussian.
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2023-06-16 |
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r-breakfast
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The breakfast package performs multiple change-point detection in data sequences, or sequence segmentation, using computationally efficient multiscale methods. This version of the package implements the "Tail-Greedy Unbalanced Haar", "Wild Binary Segmentation" and "Adaptive Wild Binary Segmentation" change-point detection and segmentation methodologies. To start with, see the function segment.mean.
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2023-06-16 |
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r-boussinesq
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This package is a collection of R functions implemented from published and available analytic solutions for the One-Dimensional Boussinesq Equation (ground-water). In particular, the function "beq.lin" is the analytic solution of the linearized form of Boussinesq Equation between two different head-based boundary (Dirichlet) conditions; "beq.song" is the non-linear power-series analytic solution of the motion of a wetting front over a dry bedrock (Song at al, 2007, see complete reference on function documentation). Bugs/comments/questions/collaboration of any kind are warmly welcomed.
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2023-06-16 |
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r-bootpr
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Bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series
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2023-06-16 |
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r-bolstad2
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A set of R functions and data sets for the book Understanding Computational Bayesian Statistics, Bolstad, W.M. (2009), John Wiley & Sons ISBN 978-0470046098
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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-bidimregression
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Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).
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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-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-bigvar
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Estimates VAR and VARX models with structured Lasso Penalties.
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2023-06-16 |
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r-bigtcr
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For studying recurrent disease and death with competing risks, comparisons based on the well-known cumulative incidence function can be confounded by different prevalence rates of the competing events. Alternatively, comparisons of the conditional distribution of the survival time given the failure event type are more relevant for investigating the prognosis of different patterns of recurrence disease. This package implements a nonparametric estimator for the conditional cumulative incidence function and a nonparametric conditional bivariate cumulative incidence function for the bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.
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2023-06-16 |
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r-bms
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Bayesian model averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison.
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2023-06-16 |
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r-biogas
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High- and low-level functions for processing biogas data and predicting biogas production. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to (possibly user-defined) standard temperature and pressure. Gas quantity can be converted between volume, mass, and moles. Gas composition, cumulative production, or other variables can be interpolated to a specified time. Cumulative biogas and methane production (and rates) can be calculated using volumetric, manometric, or gravimetric methods for any number of reactors. With cumulative methane production data and data on reactor contents, biochemical methane potential (BMP) can be calculated and summarized, including subtraction of the inoculum contribution and normalization by substrate mass. Cumulative production and production rates can be summarized in several different ways (e.g., omitting normalization) using the same function. Biogas quantity and composition can be predicted from substrate composition and additional, optional data. Lastly, inoculum and substrate mass can be determined for planning BMP experiments.
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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-assertive.strings
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A set of predicates and assertions for checking the properties of strings. 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-bsda
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Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.
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2023-06-16 |
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r-brandwatchr
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Interact with the 'Brandwatch' API <https://developers.brandwatch.com/docs>. Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories. Also allows you to directly obtain mentions and aggregate data for a specified query or query group.
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2023-06-16 |
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r-bpcp
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Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time. Especially important for small sample sizes or heavily censored data. Includes mid-p options.
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2023-06-16 |
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r-bootmrmr
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Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).
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2023-06-16 |
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r-blakerci
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Fast and accurate calculation of Blaker's binomial and Poisson confidence limits (and some related stuff).
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2023-06-16 |
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r-binaryemvs
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Implements variable selection for high dimensional datasets with a binary response variable using the EM algorithm. Both probit and logit models are supported. Also included is a useful function to generate high dimensional data with correlated variables.
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2023-06-16 |
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r-bnlearn
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Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <http://www.bnlearn.com>.
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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-blockmatrix
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Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed.
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2023-06-16 |
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r-biocircos
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Implement in 'R' interactive Circos-like visualizations of genomic data, to map information such as genetic variants, genomic fusions and aberrations to a circular genome, as proposed by the 'JavaScript' library 'BioCircos.js', based on the 'JQuery' and 'D3' technologies. The output is by default displayed in stand-alone HTML documents or in the 'RStudio' viewer pane. Moreover it can be integrated in 'R Markdown' documents and 'Shiny' applications.
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2023-06-16 |
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r-binmto
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Asymptotic simultaneous confidence intervals for comparison of many treatments with one control, for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals. Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size iteration based on approximate any-pair power. Exact conditional maximum test for many-to-one comparisons to a control.
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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 |
