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r-bestree
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Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process. Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) <arXiv:1804.10168>.
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2024-01-16 |
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r-benchmarkmedata
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Crowd sourced benchmarks from running the 'benchmarkme' package.
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2024-01-16 |
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r-benchmarkme
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Benchmark your CPU and compare against other CPUs. Also provides functions for obtaining system specifications, such as RAM, CPU type, and R version.
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2024-01-16 |
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r-berryfunctions
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Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.
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2024-01-16 |
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r-bentcablear
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Included are two main interfaces, bentcable.ar() and bentcable.dev.plot(), for fitting and diagnosing bent-cable regressions for autoregressive time-series data (Chiu and Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time series or otherwise - Chiu, Lockhart and Routledge 2006, <doi:10.1198/016214505000001177>). 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 corrected 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 improved robustness of the error-message producing mechanism. Version 0.3.1 improves the NAMESPACE file of the package. It is the author's intention to distribute any future updates via GitHub.
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2024-01-16 |
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r-benford.analysis
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Provides tools that make it easier to validate data using Benford's Law.
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2024-01-16 |
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r-beastjar
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Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST' software library of Suchard et al (2018) <doi:10.1093/ve/vey016>. 'BEAST' supports auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo sampling for a large variety of composable standard and phylogenetic statistical models using high performance computing. By placing the 'BEAST' JAR in this package, we offer an efficient distribution system for 'BEAST' use by other R packages using CRAN.
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2024-01-16 |
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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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2024-01-16 |
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r-bedr
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Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'. These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging. bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing.
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2024-01-16 |
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r-bde
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A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.
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2024-01-16 |
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r-benchden
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Full implementation of the 28 distributions introduced as benchmarks for nonparametric density estimation by Berlinet and Devroye (1994) <https://hal.science/hal-03659919>. Includes densities, cdfs, quantile functions and generators for samples as well as additional information on features of the densities. Also contains the 4 histogram densities used in Rozenholc/Mildenberger/Gather (2010) <doi:10.1016/j.csda.2010.04.021>.
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2024-01-16 |
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r-belex
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Tools for downloading historical financial data from the www.belex.rs.
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2024-01-16 |
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r-behavr
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Implements an S3 class based on 'data.table' to store and process efficiently ethomics (high-throughput behavioural) data.
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2024-01-16 |
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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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2024-01-16 |
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r-bazar
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A collection of miscellaneous functions for copying objects to the clipboard ('Copy'); manipulating strings ('concat', 'mgsub', 'trim', 'verlan'); loading or showing packages ('library_with_dep', 'require_with_dep', 'sessionPackages'); creating or testing for named lists ('nlist', 'as.nlist', 'is.nlist'), formulas ('is.formula'), empty objects ('as.empty', 'is.empty'), whole numbers ('as.wholenumber', 'is.wholenumber'); testing for equality ('almost.equal', 'almost.zero') and computing uniqueness ('almost.unique'); getting modified versions of usual functions ('rle2', 'sumNA'); making a pause or a stop ('pause', 'stopif'); converting into a function ('as.fun'); providing a C like ternary operator ('condition %?% true %:% false'); finding packages and functions ('get_all_pkgs', 'get_all_funs'); and others ('erase', '%nin%', 'unwhich', 'top', 'bot', 'normalize').
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2024-01-16 |
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r-beast
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Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2017) <arXiv:1709.06111> for a detailed presentation of the method.
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2024-01-16 |
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r-beanplot
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Plots univariate comparison graphs, an alternative to boxplot/stripchart/violin plot.
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2024-01-16 |
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r-bayesx
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Functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX.
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2024-01-16 |
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r-bdpv
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Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values.
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2024-01-16 |
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r-bdp2
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Tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.
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2024-01-16 |
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r-bdots
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Analyze differences among time series curves with p-value adjustment for multiple comparisons introduced in Oleson et al (2015) <DOI:10.1177/0962280215607411>.
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2024-01-16 |
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r-bcra
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Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals. Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.
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2024-01-16 |
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r-bcgee
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Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.
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2024-01-16 |
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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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2024-01-16 |
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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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2024-01-16 |
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r-bcaboot
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Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).
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2024-01-16 |
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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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2024-01-16 |
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r-bb
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Barzilai-Borwein spectral methods for solving nonlinear system of equations, and for optimizing nonlinear objective functions subject to simple constraints. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("BB").
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2024-01-16 |
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r-bayestestr
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Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.
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2024-01-16 |
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r-baystar
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Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.
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2024-01-16 |
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r-bayfoxr
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A Bayesian, global planktic foraminifera core top calibration to modern sea-surface temperatures. Includes four calibration models, considering species-specific calibration parameters and seasonality.
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2024-01-16 |
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r-bayesrules
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Provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models.
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2024-01-16 |
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r-bayestools
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Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.
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2024-01-16 |
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r-bayestreeprior
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Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).
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2024-01-16 |
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r-batchjobs
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Provides Map, Reduce and Filter variants to generate jobs on batch computing systems like PBS/Torque, LSF, SLURM and Sun Grid Engine. Multicore and SSH systems are also supported. For further details see the project web page.
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2024-01-16 |
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r-bayest
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Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.
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2024-01-16 |
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r-bayesplot
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Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.
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2024-01-16 |
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r-bayess
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Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.
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2024-01-16 |
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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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2024-01-16 |
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r-bat
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Includes algorithms to assess alpha and beta diversity in all their dimensions (taxonomic, phylogenetic and functional). It allows performing a number of analyses based on species identities/abundances, phylogenetic/functional distances, trees, convex-hulls or kernel density n-dimensional hypervolumes depicting species relationships. Cardoso et al. (2015) <doi:10.1111/2041-210X.12310>.
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2024-01-16 |
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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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2024-01-16 |
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r-batchgetsymbols
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Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.
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2024-01-16 |
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r-bayesianpower
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A collection of methods to determine the required sample size for the evaluation of inequality constrained hypotheses by means of a Bayes factor. Alternatively, for a given sample size, the unconditional error probabilities or the expected conditional error probabilities can be determined. Additional material on the methods in this package is available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) <doi:10.31219/osf.io/d5kf3>.
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2024-01-16 |
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r-bayesdistreg
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Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.
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2024-01-16 |
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r-bayesda
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Functions for Bayesian Data Analysis, with datasets from the book "Bayesian data Analysis (second edition)" by Gelman, Carlin, Stern and Rubin. Not all datasets yet, hopefully completed soon.
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2024-01-16 |
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r-bayescombo
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Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis.
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2024-01-16 |
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r-bayesbio
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A hodgepodge of hopefully helpful functions. Two of these perform shrinkage estimation: one using a simple weighted method where the user can specify the degree of shrinkage required, and one using James-Stein shrinkage estimation for the case of unequal variances.
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2024-01-16 |
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r-batchscr
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Handy frameworks, such as error handling and log generation, for batch scripts. Use case: in scripts running in remote servers, set error handling mechanism for downloading and uploading and record operation log.
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2024-01-16 |
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r-batchmeans
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Provides consistent batch means estimation of Monte Carlo standard errors.
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2024-01-16 |
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r-basedosdados
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An R interface to the 'Base dos Dados' API <https:basedosdados.github.io/mais/py_reference_api/>). Authenticate your project, query our tables, save data to disk and memory, all from R.
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2024-01-16 |
