matrixStats - Functions that Apply to Rows and Columns of Matrices (and to Vectors)

High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().

Last updated 4 months ago

matrixperformancevector

200 stars 14.74 score 0 dependencies 2127 dependents

future - Unified Parallel and Distributed Processing in R for Everyone

The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multisession)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers, etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures.

Last updated 4 months ago

asynchronousdistributed-computingfutureshpchpc-clustersparallel-computingparallel-processingparallelizationprogrammingpromises

948 stars 13.18 score 5 dependencies 1039 dependents

parallelly - Enhancing the 'parallel' Package

Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package. For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.

Last updated 5 months ago

parallel-computing

124 stars 12.61 score 0 dependencies 1077 dependents

globals - Identify Global Objects in R Expressions

Identifies global ("unknown" or "free") objects in R expressions by code inspection using various strategies (ordered, liberal, or conservative). The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in parallel, distributed compute environments.

Last updated 2 years ago

code-inspection

29 stars 12.49 score 1 dependencies 1062 dependents

listenv - Environments Behaving (Almost) as Lists

List environments are environments that have list-like properties. For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.

Last updated 6 months ago

28 stars 12.38 score 0 dependencies 1029 dependents

future.apply - Apply Function to Elements in Parallel using Futures

Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.

Last updated 4 months ago

asynchronousdistributed-computingfuturehpchpc-clustersparallelparallel-computingparallel-processingparallelizationprogramming

206 stars 11.86 score 6 dependencies 804 dependents

progressr - An Inclusive, Unifying API for Progress Updates

A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar(), cli::cli_progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beepr::beep(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. The package is compatible with Shiny applications.

Last updated 12 months ago

conditionsprogress

278 stars 11.29 score 1 dependencies 623 dependents

R.oo - R Object-Oriented Programming with or without References

Methods and classes for object-oriented programming in R with or without references. Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers. The package has been developed since 2001 and is now considered very stable. This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.

Last updated 6 months ago

20 stars 11.19 score 1 dependencies 688 dependents

R.methodsS3 - S3 Methods Simplified

Methods that simplify the setup of S3 generic functions and S3 methods. Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers. For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible. The method setMethodS3() is a good start for those who in the future may want to migrate to S4. This is a cross-platform package implemented in pure R that generates standard S3 methods.

Last updated 2 years ago

1 stars 11.15 score 0 dependencies 684 dependents

R.utils - Various Programming Utilities

Utility functions useful when programming and developing R packages.

Last updated 8 months ago

61 stars 11.13 score 2 dependencies 654 dependents

R.cache - Fast and Light-Weight Caching (Memoization) of Objects and Results to Speed Up Computations

Memoization can be used to speed up repetitive and computational expensive function calls. The first time a function that implements memoization is called the results are stored in a cache memory. The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations. With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects. The cache memory is persistent (on the file system).

Last updated 2 years ago

cachememoization

38 stars 6.67 score 4 dependencies 99 dependents

doFuture - Use Foreach to Parallelize via the Future Framework

The 'future' package provides a unifying parallelization framework for R that supports many parallel and distributed backends. The 'foreach' package provides a powerful API for iterating over an R expression in parallel. The 'doFuture' package brings the best of the two together. There are two alternative ways to use this package. The recommended approach is to use 'y <- foreach(...) %dofuture% { ... }', which does not require using 'registerDoFuture()' and has many advantages over '%dopar%'. The alternative is the traditional 'foreach' approach by registering the 'foreach' adapter 'registerDoFuture()' and so that 'y <- foreach(...) %dopar% { ... }' runs in parallelizes with the 'future' framework.

Last updated 7 months ago

batchjobsbatchtoolsbiocparalleldistributed-computingforeachhpchpc-clustersparallelplyr

83 stars 6.48 score 9 dependencies 77 dependents

illuminaio - Parsing Illumina Microarray Output Files

Tools for parsing Illumina's microarray output files, including IDAT.

Last updated 3 months ago

bioconductor-package

5.06 score 4 dependencies 44 dependents

R.matlab - Read and Write MAT Files and Call MATLAB from Within R

Methods readMat() and writeMat() for reading and writing MAT files. For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.

Last updated 2 years ago

matlab

86 stars 4.98 score 3 dependencies 22 dependents

startup - Friendly R Startup Configuration

Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files. This makes it possible to keep private / secret environment variables separate from other environment variables. It also makes it easier to share specific startup settings by simply copying a file to a directory.

Last updated 8 months ago

configurationenvironment-variablesstartuputility

161 stars 4.96 score 0 dependencies

illuminaio - Parsing Illumina Microarray Output Files

Tools for parsing Illumina's microarray output files, including IDAT.

Last updated 1 years ago

bioconductor-packagebioconductor

6 stars 4.75 score 4 dependencies 44 dependents

TopDom - An Efficient and Deterministic Method for Identifying Topological Domains in Genomes

The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 <doi:10.1093/nar/gkv1505>). The authors published an implementation of their method as an R script (two different versions; also available in this package). This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation. It also provides a small number of bug fixes to the original code.

Last updated 3 years ago

genomicshictopological-domains

18 stars 4.33 score 34 dependencies 1 dependents

future.batchtools - A Future API for Parallel and Distributed Processing using 'batchtools'

Implementation of the Future API on top of the 'batchtools' 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)'.

Last updated 7 months ago

distributed-computinghpcjob-schedulerparallelpbssgeslurmtorque

83 stars 3.97 score 27 dependencies

affxparser - Affymetrix File Parsing SDK

Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure.

Last updated 3 months ago

bioconductor-package

3.93 score 0 dependencies 15 dependents

future.callr - A Future API for Parallel Processing using 'callr'

Implementation of the Future API on top of the 'callr' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine. Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession') and socket connections, the 'callr' backend provided here can run more than 125 parallel R processes.

Last updated 12 months ago

asynchronousfuturesparallel-computingparallel-processingparallelizationprogrammingpromises

62 stars 3.64 score 10 dependencies 1 dependents

profmem - Simple Memory Profiling for R

A simple and light-weight API for memory profiling of R expressions. The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).

Last updated 4 years ago

memory-profilerperformanceram

33 stars 3.61 score 0 dependencies 10 dependents

R.rsp - Dynamic Generation of Scientific Reports

The RSP markup language makes any text-based document come alive. RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'. Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries. RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things. Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'. RSP is ideal for self-contained scientific reports and R package vignettes. It's easy to use - if you know how to write an R script, you'll be up and running within minutes.

Last updated 5 months ago

documentmarkupreportreproducibilityscience

30 stars 3.56 score 5 dependencies 8 dependents

aroma.light - Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes.

Last updated 1 years ago

bioconductor-packagebioconductor

1 stars 3.47 score 4 dependencies 20 dependents

aroma.light - Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes.

Last updated 3 months ago

bioconductor-package

3.38 score 4 dependencies 20 dependents

affxparser - Affymetrix File Parsing SDK

Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure.

Last updated 6 months ago

bioconductor-packagebioconductor

7 stars 3.36 score 0 dependencies 15 dependents

R.devices - Unified Handling of Graphics Devices

Functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types. Switching output format requires minimal changes in code. This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.

Last updated 6 months ago

graphics

17 stars 3.32 score 4 dependencies 13 dependents

PSCBS - Analysis of Parent-Specific DNA Copy Numbers

Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumor-only analyses are supported.

Last updated 5 months ago

7 stars 3.18 score 13 dependencies 9 dependents

R.filesets - Easy Handling of and Access to Files Organized in Structured Directories

A file set refers to a set of files located in one or more directories on the file system. This package provides classes and methods to locate, setup, subset, navigate and iterate such sets. The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats. Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags. This adds additional flexibility to identify file sets and individual files. NOTE: This package's API should be considered to be in an beta stage. Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.

Last updated 6 months ago

files

2 stars 2.25 score 5 dependencies 7 dependents

aroma.core - Core Methods and Classes Used by 'aroma.*' Packages Part of the Aroma Framework

Core methods and classes used by higher-level 'aroma.*' packages part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'.

Last updated 2 years ago

1 stars 2.09 score 20 dependencies 6 dependents

aroma.affymetrix - Analysis of Large Affymetrix Microarray Data Sets

A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006.

Last updated 5 months ago

affymetrixanalysiscopy-numberdnaexpressionhpclarge-scalemicroarraynotebookreproducibilityrna

10 stars 2.08 score 24 dependencies 3 dependents

port4me - Get the Same, Personal, Free 'TCP' Port over and over

An R implementation of the cross-platform, language-independent "port4me" algorithm (<https://github.com/HenrikBengtsson/port4me>), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration.

Last updated 5 months ago

bashclihigh-performance-computinghpcmulti-tenantmulti-userportpypi-packagepythonr-languager-programmingtcputility

13 stars 2.01 score 0 dependencies

R.huge - 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.)

Last updated 6 months ago

1.83 score 3 dependencies 5 dependents

future.tests - Test Suite for 'Future API' Backends

Backends implementing the 'Future' API, as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API. The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines.

Last updated 1 years ago

futuretesting

10 stars 1.82 score 10 dependencies

aroma.apd - A Probe-Level Data File Format Used by 'aroma.affymetrix' [deprecated]

DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities. Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets). In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit. This speeds up the read substantially. This package is supporting the Aroma framework and should not be used elsewhere.)

Last updated 1 years ago

1.66 score 4 dependencies 4 dependents

seguid - Sequence Globally Unique Identifier ('SEGUID') Checksums for Linear, Circular, Single-Stranded and Double-Stranded Biological Sequences

An R implementation of the original Sequence Globally Unique Identifier ('SEGUID') algorithm [Babnigg and Giometti (2006) <doi:10.1002/pmic.200600032>] and 'SEGUID' v2 (<https://www.seguid.org>), which extends 'SEGUID' v1 with support for linear, circular, single- and double-stranded biological sequences, e.g. DNA, RNA, and proteins.

Last updated 5 months ago

seguid

1.64 score 2 dependencies

marshal - Framework to Marshal Objects to be Used in Another R Process

Some types of R objects can be used only in the R session they were created. If used as-is in another R process, such objects often result in an immediate error or in obscure and hard-to-troubleshoot outcomes. Because of this, they cannot be saved to file and re-used at a later time. They can also not be exported to a worker in parallel processing. These objects are sometimes referred to as non-exportable or non-serializable objects. One solution to this problem is to use "marshalling" to encode the R object into an exportable representation that then can be used to re-create a copy of that object in another R process. This package provides a framework for marshalling and unmarshalling R objects such that they can be transferred using functions such as serialize() and unserialize() of base R.

Last updated 10 months ago

marshallingparallelserialization

14 stars 1.58 score 0 dependencies

marshal - Framework to Marshal Objects to be Used in Another R Process

Some types of R objects can be used only in the R session they were created. If used as-is in another R process, such objects often result in an immediate error or in obscure and hard-to-troubleshoot outcomes. Because of this, they cannot be saved to file and re-used at a later time. They can also not be exported to a worker in parallel processing. These objects are sometimes referred to as non-exportable or non-serializable objects. One solution to this problem is to use "marshalling" to encode the R object into an exportable representation that then can be used to re-create a copy of that object in another R process. This package provides a framework for marshalling and unmarshalling R objects such that they can be transferred using functions such as serialize() and unserialize() of base R.

Last updated 10 months ago

marshallingparallelserialization

14 stars 1.58 score 0 dependencies

ACNE - 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).

Last updated 5 months ago

1.54 score 25 dependencies

aroma.cn - Copy-Number Analysis of Large Microarray Data Sets

Methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.

Last updated 5 months ago

1 stars 0.95 score 22 dependencies

calmate - Improved Allele-Specific Copy Number of SNP Microarrays for Downstream Segmentation

The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina.

Last updated 2 years ago

1 stars 0.85 score 22 dependencies

future.tools - Tools for Working with Futures

Tools for Working with Futures.

Last updated 2 months ago

parallel-computingparallel-programming

2 stars 0.71 score 37 dependencies

dChipIO - Methods for Reading dChip Files

Functions for reading DCP and CDF.bin files generated by the dChip software.

Last updated 9 years ago

0.62 score 0 dependencies

doFuture.tests.extra - Extra Test Sets for the 'doFuture' Package

Runs examples of packages that use 'foreach' and '%dopar%' for parallelization, where 'doFuture' is used as the 'foreach' adapter making it possible to use any future backend for parallelization. The package tests use these tools to test 'doFuture' with 'foreach'-based examples from packages 'BiocParallel', 'caret', 'doParallel', 'glmnet', 'NMF', 'plyr', and 'TSP'. These tests are run with many known future backends.

Last updated 8 months ago

futureverseparalleltestsuite

0.09 score 10 dependencies

sfit - Multidimensional Simplex Fitting

Methods for robustly fitting a K-dimensional simplex in M dimensions.

Last updated 2 years ago

cconemodelsimplex

1 stars 0.09 score 2 dependencies

HaarSeg - Fast and Flexible Microarray Segmentation

A fast and flexible method for the segmentation of aCGH data using the HaarSeg method by Ben-Yaacov and Eldar (2008) <doi:10.1093/bioinformatics/btn272>.

Last updated 2 years ago

legacy

0.00 score 0 dependencies

expectile - Modelling of Expectiles

Methods for fitting a simplex or polyhedral cone to multivariate data, for doing expectile regression and skyline/baseline estimation.

Last updated 2 years ago

0.00 score 1 dependencies