Modified items

All recently modified items, latest first.

R-rlecuyer-0.3.3-10.lbn19.x86_64: Provides an interface to the C implementation of the random number generator with multiple independent streams developed by L'Ecuyer et al (2002). The main purpose of this package is to enable the use of this random number generator in parallel R applications.

R-repr-0.9-1.lbn19.noarch: String and binary representations of objects for several formats / mime types.

R-qvalue-1.38.0-3.lbn19.noarch: It takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. This package is a part of the Bioconductor (bioconductor.org) project.

R-qtl-1.39.5-1.lbn19.x86_64: R-qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental crosses. Our goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing. A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses. The current version of R-qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation and Haley-Knott regression.

R-qcc-2.2-10.lbn19.noarch: An R package for quality control charting and statistical process control. The qcc package for the R statistical environment provides: - Plot Shewhart quality control charts - Plot Cusum and EMWA charts for continuous data - Draw operating characteristic curves - Perform process capability analysis - Draw Pareto charts and cause-and-effect diagrams

R-preprocessCore-1.26.1-4.lbn19.x86_64: A library of core preprocessing routines

R-praise-1.0.0-3.lbn19.noarch: Build friendly R packages that praise their users if they have done something good, or they just need it to feel better.

R-pls-2.4.3-5.lbn19.noarch: Multivariate regression by partial least squares regression (PLSR) and principal component regression (PCR)

R-pbdZMQ-0.2.3-1.lbn19.x86_64: 'ZeroMQ' is a well-known library for high-performance asynchronous messaging in scalable, distributed applications. This package provides high level R wrapper functions to easily utilize 'ZeroMQ'. We mainly focus on interactive client/server programming frameworks. For convenience, a minimal 'ZeroMQ' library (4.1.0 rc1) is shipped with 'pbdZMQ', which can be used if no system installation of 'ZeroMQ' is available. A few wrapper functions compatible with 'rzmq' are also provided.

R-nws-1.7.0.1-11.lbn19.noarch: Provides coordination and parallel execution facilities, as well as limited cross-language data exchange, using the netWorkSpaces server developed by REvolution Computing.

R-mvtnorm-1.0.3-2.lbn19.x86_64: This R package computes multivariate normal and t probabilities, quantiles and densities.

R-multtest-2.20.0-4.lbn19.x86_64: Non-parametric bootstrap and permutation resampling-based multiple testing procedures for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Single-step and step-wise methods are implemented. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models) are included. Results are reported in terms of adjusted p-values, confindence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. This Library is a part of the Bioconductor (bioconductor.org) proejct.

R-multcomp-1.4.5-2.lbn19.noarch: This R package contains functions for simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models.

R-msm-1.3-5.lbn19.x86_64: Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. Both Markov transition rates and the hidden Markov output process can be modeled in terms of covariates. A variety of observation schemes are supported, including processes observed at arbitrary times, completely-observed processes, and censored states.

R-memoise-0.2.1-3.lbn19.noarch: Cache the results of a function so that when you call it again with the same arguments it returns the pre-computed value.

R-magrittr-1.5-1.lbn19.noarch: Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions. For more information, see package vignette. To quote Rene Magritte, "Ceci n'est pas un pipe."

R-maanova-1.34.1-4.lbn19.x86_64: Analysis of N-dye Micro Array experiment using mixed model effect Containing analysis of variance, permutation and bootstrap, cluster and consensus tree

R-mAr-1.1.2-11.lbn19.x86_64: R package: An R add-on package for estimation of multivariate AR models through a computationally-efficient stepwise least-squares algorithm (Neumaier and Schneider, 2001); the procedure is of particular interest for high-dimensional data without missing values such as geophysical fields.

R-lmtest-0.9.34-2.lbn19.x86_64: A collection of tests, data sets and examples for diagnostic checking in linear regression models in R.

R-littler-0.3.0-2.lbn19.x86_64: A scripting and command-line front-end is provided by 'r' (aka 'littler') as a lightweight binary wrapper around the GNU R language and environment for statistical computing and graphics. While R can be used in batch mode, the r binary adds full support for both 'shebang'-style scripting (i.e. using a hash-mark-exclamation-path expression as the first line in scripts) as well as command-line use in standard Unix pipelines. In other words, r provides the R language without the environment.


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