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Affymetrix SNP Probe-Summarization using Non-Negative Matrix Factorization
A summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF).
Improved Allele-Specific Copy Number of SNP Microarrays for Downstream Segmentation
A multi-array post-processing method of allele-specific copy-number estimates (ASCNs).
Methods for Accessing Huge Amounts of Data [deprecated]
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor). The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system. This way the size limit of the data is set by the file system and not the memory.)
A Future API for Parallel and Distributed Processing using BatchJobs
Implementation of the Future API on top of the 'BatchJobs' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'. NOTE: The 'BatchJobs' package is deprecated in favor of the 'batchtools' package. Because of this, it is recommended to use the 'future.batchtools' package instead of this package.
Fits a Principal Curve in Arbitrary Dimension
Fitting a principal curve to a data matrix in arbitrary dimensions.
Hastie and Stuetzle (1989)
R Interface with Google Compute Engine
Interact with the 'Google Compute Engine' API in R. Lets you create, start and stop instances in the 'Google Cloud'. Support for preconfigured instances, with templates for common R needs.
Sudoku Puzzle Generator and Solver
Generates, plays, and solves Sudoku puzzles. The GUI playSudoku() needs package "tkrplot" if you are not on Windows.
Reversible Jump MCMC for the Analysis of CGH Arrays
Bayesian analysis of CGH microarrays fitting Hidden Markov Chain models. The selection of the number of states is made via their posterior probability computed by Reversible Jump Markov Chain Monte Carlo Methods. Also returns probabilistic common regions for gains/losses.
Improved Access for Blind Users
Blind users do not have access to the graphical output from R without printing the content of graphics windows to an embosser of some kind. This is not as immediate as is required for efficient access to statistical output. The functions here are created so that blind people can make even better use of R. This includes the text descriptions of graphs, convenience functions to replace the functionality offered in many GUI front ends, and experimental functionality for optimising graphical content to prepare it for embossing as tactile images.
Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview). The README describes the history of the package development process.