Hi Ivan, Just look at the output of the source()
> source("http://bioconductor.org/biocLite.R") BioC_mirror = http://bioconductor.org Change using chooseBioCmirror(). Cheers On Fri, Apr 15, 2011 at 5:41 PM, Ivan Gregoretti <ivang...@gmail.com> wrote: > Great. > > Would you mind showing how you can update the packages using any of > those mirrors? > > Thank you, > > Ivan > > > > On Fri, Apr 15, 2011 at 11:34 AM, Dan Tenenbaum <dtene...@fhcrc.org> > wrote: > > On Fri, Apr 15, 2011 at 8:10 AM, Ivan Gregoretti <ivang...@gmail.com> > wrote: > >> Hello Dan, > >> > >> Updating Bioconductor devel packages as instructed in > >> > >> http://bioconductor.org/install/ > >> > >> usually leads to stalled downloads when it retrieves large packages. > >> The BSgenome packages are notorious for this. > >> > >> Can you or anybody recommend a solution that does not involve manually > >> downloading the tar balls one by one? (Perhaps the is a mirror for > >> devel.) > > > > Hi Ivan, > > > > There are mirrors for release and devel here: > > > > http://bioconductor.org/about/mirrors/ > > > > Dan > > > > > >> > >> Thank you, > >> > >> Ivan > >> > >> > >> > >> On Thu, Apr 14, 2011 at 5:33 PM, Dan Tenenbaum <dtene...@fhcrc.org> > wrote: > >>> Bioconductors: > >>> > >>> We are pleased to announce Bioconductor 2.8, consisting of 466 > >>> software packages and more than 500 up-to-date annotation packages. > >>> There are 48 new software packages, and many updates and improvements > >>> to existing packages. Two software packages that were in the previous > >>> version have been removed. Bioconductor 2.8 is compatible with > >>> R 2.13.0, and is supported on Linux, 32- and 64-bit Windows, and Mac > >>> OS. Visit > >>> > >>> http://bioconductor.org > >>> > >>> for details and downloads. > >>> > >>> Contents > >>> ======== > >>> > >>> * Getting Started with Bioconductor 2.8 > >>> * New Software Packages > >>> * Using Bioconductor in the cloud > >>> > >>> Getting Started with Bioconductor 2.8 > >>> ===================================== > >>> > >>> To install Bioconductor 2.8: > >>> > >>> 1. Install R 2.13.0. Bioconductor 2.8 has been designed expressly for > >>> this version of R. > >>> > >>> 2. Follow the instructions here: > >>> > >>> http://bioconductor.org/install/ > >>> > >>> Please visit http://bioconductor.org for details and downloads. > >>> > >>> New Software Packages > >>> ===================== > >>> > >>> There are 48 new packages in this release of Bioconductor. > >>> > >>> a4 > >>> > >>> Automated Affymetrix Array Analysis Umbrella Package > >>> > >>> a4Base > >>> > >>> Automated Affymetrix Array Analysis Base Package > >>> > >>> a4Classif > >>> > >>> Automated Affymetrix Array Analysis Classification Package > >>> > >>> a4Core > >>> > >>> Automated Affymetrix Array Analysis Core Package > >>> > >>> a4Preproc > >>> > >>> Automated Affymetrix Array Analysis Preprocessing Package > >>> > >>> a4Reporting > >>> > >>> Automated Affymetrix Array Analysis Reporting Package > >>> > >>> AnnotationFuncs > >>> > >>> Annotation translation functions > >>> > >>> anota > >>> > >>> ANalysis Of Translational Activity > >>> > >>> chopsticks > >>> > >>> The snp.matrix and X.snp.matrix classes > >>> > >>> Clonality > >>> > >>> Clonality testing > >>> > >>> clst > >>> > >>> Classification by local similarity threshold > >>> > >>> clstutils > >>> > >>> Tools for performing taxonomic assignment > >>> > >>> clusterProfiler > >>> > >>> statistical analysis and visulization of > >>> functional profiles for genes and gene clusters > >>> > >>> cn.farms > >>> > >>> Factor Analysis for copy number estimation > >>> > >>> ENVISIONQuery > >>> > >>> Retrieval from the ENVISION bioinformatics data portal into R > >>> > >>> ExiMiR > >>> > >>> R functions for the normalization of Exiqon miRNA array data > >>> > >>> flowPhyto > >>> > >>> Methods for Continuous Flow Cytometry > >>> > >>> flowPlots > >>> > >>> analysis plots and data class for gated flow cytometry data > >>> > >>> gaia > >>> > >>> An R package for genomic analysis of significant > >>> chromosomal aberrations > >>> > >>> genefu > >>> > >>> Relevant Functions for Gene Expression Analysis, > >>> Especially in Breast Cancer > >>> > >>> genoset > >>> > >>> Provides classes similar to ExpressionSet for copy number analysis > >>> > >>> GSVA > >>> > >>> Gene Set Variation Analysis > >>> > >>> ibh > >>> > >>> Interaction Based Homogeneity for Evaluating Gene Lists > >>> > >>> inveRsion > >>> > >>> Inversions in genotype data > >>> > >>> IPPD > >>> > >>> Isotopic peak pattern deconvolution for Protein Mass > >>> Spectrometry by template matching > >>> > >>> joda > >>> > >>> JODA algorithm for quantifying gene deregulation using knowledge > >>> > >>> lol > >>> > >>> Lots Of Lasso > >>> > >>> mcaGUI > >>> > >>> Microbial Community Analysis GUI > >>> > >>> mgsa > >>> > >>> Model-based gene set analysis > >>> > >>> MLP > >>> > >>> Mean Log P Analysis > >>> > >>> mosaics > >>> > >>> MOdel-based one and two Sample Analysis and Inference for ChIP-Seq > >>> > >>> MSnbase > >>> > >>> Base Functions and Classes for MS-based Proteomics > >>> > >>> NCIgraph > >>> > >>> Pathways from the NCI Pathways Database > >>> > >>> phenoDist > >>> > >>> Phenotypic distance measures > >>> > >>> phenoTest > >>> > >>> Tools to test correlation between gene expression and phenotype > >>> > >>> procoil > >>> > >>> Prediction of Oligomerization of Coiled Coil Proteins > >>> > >>> pvac > >>> > >>> PCA-based gene filtering for Affymetrix arrays > >>> > >>> qrqc > >>> > >>> Quick Read Quality Control > >>> > >>> RNAinteract > >>> > >>> Estimate Pairwise Interactions from multidimensional features > >>> > >>> Rsubread > >>> > >>> a super fast, sensitive and accurate read aligner for mapping > >>> next-generation sequencing reads > >>> > >>> seqbias > >>> > >>> Estimation of per-position bias in high-throughput sequencing data > >>> > >>> snm > >>> > >>> Supervised Normalization of Microarrays > >>> > >>> snpStats > >>> > >>> SnpMatrix and XSnpMatrix classes and methods > >>> > >>> survcomp > >>> > >>> Performance Assessment and Comparison for Survival Analysis > >>> > >>> TDARACNE > >>> > >>> Network reverse engineering from time course data > >>> > >>> TEQC > >>> > >>> Quality control for target capture experiments > >>> > >>> TurboNorm > >>> > >>> A fast scatterplot smoother suitable for microarray normalization > >>> > >>> Vega > >>> > >>> An R package for copy number data segmentation > >>> > >>> > >>> Using Bioconductor in the cloud > >>> =============================== > >>> > >>> This release features the Bioconductor Amazon Machine > >>> Image (AMI), which allows easy access to R and Bioconductor > >>> within the Elastic Compute Cloud (EC2). It's easy to run > >>> parallelizable tasks on MPI clusters, run R from within > >>> your web browser using RStudio Server, and more. No > >>> installation required. Information available at: > >>> > >>> http://bioconductor.org/help/bioconductor-cloud-ami/ > >>> > >>> _______________________________________________ > >>> Bioc-sig-sequencing mailing list > >>> Bioc-sig-sequencing@r-project.org > >>> https://stat.ethz.ch/mailman/listinfo/bioc-sig-sequencing > >>> > >> > > > > _______________________________________________ > Bioc-sig-sequencing mailing list > Bioc-sig-sequencing@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioc-sig-sequencing > -- João Moura [[alternative HTML version deleted]]
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