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r-weco
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Western Electric Company Rules (WECO) have been widely used for Shewhart control charts in order to increase the sensitivity of detecting assignable causes of process change. This package implements eight commonly used WECO rules and allow to apply the combination of these individual rules for detecting the deviation from a stable process. The package also provides a web-based graphical user interface to help users conduct the analysis.
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2025-04-22 |
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r-webutils
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Parses http request data in application/json, multipart/form-data, or application/x-www-form-urlencoded format. Includes example of hosting and parsing html form data in R using either 'httpuv' or 'Rhttpd'.
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2025-04-22 |
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r-websocket
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Provides a 'WebSocket' client interface for R. 'WebSocket' is a protocol for low-overhead real-time communication: <https://en.wikipedia.org/wiki/WebSocket>.
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2025-04-22 |
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r-webp
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Lossless webp images are 26% smaller in size compared to PNG. Lossy webp images are 25-34% smaller in size compared to JPEG. This package reads and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using conventions from the 'jpeg' and 'png' packages.
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2025-04-22 |
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r-wdm
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Provides efficient implementations of weighted dependence measures and related asymptotic tests for independence. Implemented measures are the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and Hollander et al. (2015, ISBN:9780470387375).
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2025-04-22 |
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r-wcorr
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Calculates Pearson, Spearman, polychoric, and polyserial correlation coefficients, in weighted or unweighted form. The package implements tetrachoric correlation as a special case of the polychoric and biserial correlation as a specific case of the polyserial.
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2025-04-22 |
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r-wbs
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Provides efficient implementation of the Wild Binary Segmentation and Binary Segmentation algorithms for estimation of the number and locations of multiple change-points in the piecewise constant function plus Gaussian noise model.
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2025-04-22 |
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r-wavethresh
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Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.
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2025-04-22 |
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r-waveslim
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Basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002). All figures in chapters 4-7 of GSW (2001) are reproducible using this package and R code available at the book website(s) below.
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2025-04-22 |
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r-wavelets
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Contains functions for computing and plotting discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transforms (MODWT), as well as their inverses. Additionally, it contains functionality for computing and plotting wavelet transform filters that are used in the above decompositions as well as multiresolution analyses.
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2025-04-22 |
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r-waffect
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waffect (pronounced 'double-u affect' for 'weighted affectation') is a package to simulate phenotypic (case or control) datasets under a disease model H1 such that the total number of cases is constant across all the simulations (the constrain in the title). The package also makes it possible to generate phenotypes in the case of more than two classes, so that the number of phenotypes belonging to each class is constant across all the simulations. waffect is used to assess empirically the statistical power of Genome Wide Association studies.
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2025-04-22 |
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r-volesti
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Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume of polytopes given by a set of points or linear inequalities or Minkowski sum of segments (zonotopes). There are two algorithms for volume estimation (I.Z. Emiris and V. Fisikopoulos (2014) <arXiv:1312.2873> and B. Cousins, S. Vempala (2016) <arXiv:1409.6011>) as well as algorithms for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for estimating copulas (L. Cales, A. Chalkis, I.Z. Emiris, V. Fisikopoulos (2018) <arXiv:1803.05861>).
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2025-04-22 |
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r-vnm
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Provide tools for finding multiple-objective optimal designs for estimating the shape of dose-response, the ED50 (the dose producing an effect midway between the expected responses at the extreme doses) and the MED (the minimum effective dose level) for the 2,3,4-parameter logistic models and for evaluating its efficiencies for the three objectives. The acronym VNM stands for V-algorithm using Newton Raphson method to search multiple-objective optimal design.
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2025-04-22 |
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r-vlmc
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Functions, Classes & Methods for estimation, prediction, and simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
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2025-04-22 |
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r-vlad
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Contains functions to set up risk-adjusted quality control charts in health care. For the variable life adjusted display (VLAD) proposed by Lovegrove et al. (1997) <doi:10.1016/S0140-6736(97)06507-0> signaling rules derived in Wittenberg et al. (2018) <doi: 10.1002/sim.7647> are implemented. Additionally, for the risk-adjusted cumulative sum chart based on log-likelihood ratio statistic introduced by Steiner et al. (2000) <doi:10.1093/biostatistics/1.4.441> average run length and control limits can be computed.
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2025-04-22 |
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r-vita
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Implements the novel testing approach by Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4> for the permutation variable importance measure in a random forest and the PIMP-algorithm by Altmann et al.(2010) <doi:10.1093/bioinformatics/btq134>. Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4> do not use the "standard" permutation variable importance but the cross-validated permutation variable importance for the novel test approach. The cross-validated permutation variable importance is not based on the out-of-bag observations but uses a similar strategy which is inspired by the cross-validation procedure. The novel test approach can be applied for classification trees as well as for regression trees. However, the use of the novel testing approach has not been tested for regression trees so far, so this routine is meant for the expert user only and its current state is rather experimental.
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2025-04-22 |
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r-vigor
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Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
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2025-04-22 |
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r-vifcp
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Contains a function to support the following paper: Xiaoping Shi, Xiang-Sheng Wang, Dongwei Wei, Yuehua Wu (2016), <DOI:10.1007/s00180-015-0587-5>, A sequential multiple change-point detection procedure via VIF regression, Computational Statistics, 31(2): 671-691.
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2025-04-22 |
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r-vicmodel
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The Variable Infiltration Capacity (VIC) model is a macroscale hydrologic model that solves full water and energy balances, originally developed by Xu Liang at the University of Washington (UW). The version of VIC source code used is of 5.0.1 on <https://github.com/UW-Hydro/VIC/>, see Hamman et al. (2018). Development and maintenance of the current official version of the VIC model at present is led by the UW Hydro (Computational Hydrology group) in the Department of Civil and Environmental Engineering at UW. VIC is a research model and in its various forms it has been applied to most of the major river basins around the world, as well as globally. If you make use of this model, please acknowledge the appropriate references listed in the help page of this package or on the references page <http://vic.readthedocs.io/en/master/Documentation/References/> of the VIC official documentation website. These should include Liang et al. (1994) plus any references relevant to the features you are using Reference: Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99(D7), 14415-14428, <doi:10.1029/94JD00483>. Hamman et al. (2018) about VIC 5.0.1 also can be considered: Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, <doi:10.5194/gmd-11-3481-2018>.
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2025-04-22 |
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r-vgamextra
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Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes.
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2025-04-22 |
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r-vetr
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Declarative template-based framework for verifying that objects meet structural requirements, and auto-composing error messages when they do not.
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2025-04-22 |
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r-verylargeintegers
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Multi-precission library that allows to store and operate with arbitrarily big integers without loss of precision. It includes a large list of tools to work with them, like: - Arithmetic and logic operators - Modular-arithmetic operators - Computer Number Theory utilities - Probabilistic primality tests - Factorization algorithms - Random generators of diferent types of integers.
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2025-04-22 |
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r-vdgraph
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Uses a modification of the published FORTRAN code in "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993, to produce variance dispersion graphs. Also produces fraction of design space plots, and contains data frames for several minimal run response surface designs.
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2025-04-22 |
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r-vca
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ANOVA and REML estimation of linear mixed models is implemented, once following Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package. The primary objective of this package is to perform a variance component analysis (VCA) according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement Procedures" (2014). There are plotting methods for visualization of an experimental design, plotting random effects and residuals. For ANOVA type estimation two methods for computing ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of variance components can be derived, which is used in estimating confidence intervals. Linear hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific values of covariables and with custom weighting schemes for factor variables. See ?VCA for a more comprehensive description of the features.
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2025-04-22 |
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r-vbsr
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Efficient algorithm for solving ultra-sparse regularized regression models using a variational Bayes algorithm with a spike (l0) prior. Algorithm is solved on a path, with coordinate updates, and is capable of generating very sparse models. There are very general model diagnostics for controling type-1 error included in this package.
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2025-04-22 |
