Showing 18 of total 18 results (show query)
revolutionanalytics
foreach:Provides Foreach Looping Construct
Support for the foreach looping construct. Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. This package in particular is intended to be used for its return value, rather than for its side effects. In that sense, it is similar to the standard lapply function, but doesn't require the evaluation of a function. Using foreach without side effects also facilitates executing the loop in parallel.
Maintained by Folashade Daniel. Last updated 3 years ago.
53 stars 17.21 score 43k scripts 2.8k dependents
shikokuchuo
mirai:Minimalist Async Evaluation Framework for R
Designed for simplicity, a 'mirai' evaluates an R expression asynchronously in a parallel process, locally or distributed over the network. The result is automatically available upon completion. Modern networking and concurrency, built on 'nanonext' and 'NNG' (Nanomsg Next Gen), ensures reliable and efficient scheduling over fast inter-process communications or TCP/IP secured by TLS. Distributed computing can launch remote resources via SSH or cluster managers. An inherently queued architecture handles many more tasks than available processes, and requires no storage on the file system. Innovative features include support for otherwise non-exportable reference objects, event-driven promises, and asynchronous parallel map.
Maintained by Charlie Gao. Last updated 8 days ago.
asyncasynchronous-tasksconcurrencydistributed-computinghigh-performance-computingparallel-computing
219 stars 11.89 score 130 scripts 7 dependents
privefl
bigsnpr:Analysis of Massive SNP Arrays
Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. Privé et al. (2018) <doi:10.1093/bioinformatics/bty185>.
Maintained by Florian Privé. Last updated 23 days ago.
big-databioinformaticsmemory-mapped-fileparallel-computingpolygenic-scorespopulation-structure-inferencesnp-datastatistical-methodsopenblaszlibcppopenmp
200 stars 11.44 score 1.5k scripts 3 dependents
mllg
batchtools:Tools for Computation on Batch Systems
As a successor of the packages 'BatchJobs' and 'BatchExperiments', this package provides a parallel implementation of the Map function for high performance computing systems managed by schedulers 'IBM Spectrum LSF' (<https://www.ibm.com/products/hpc-workload-management>), 'OpenLava' (<https://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid Engine' (<https://www.univa.com/>), 'Slurm' (<https://slurm.schedmd.com/>), 'TORQUE/PBS' (<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>), or 'Docker Swarm' (<https://docs.docker.com/engine/swarm/>). A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.
Maintained by Michel Lang. Last updated 2 years ago.
batchexperimentsbatchjobsdocker-swarmhigh-performance-computinghpchpc-clusterslsfopenlavaparallel-computingreproducibilitysgeslurmtorque
175 stars 11.39 score 772 scripts 14 dependentsprivefl
bigstatsr:Statistical Tools for Filebacked Big Matrices
Easy-to-use, efficient, flexible and scalable statistical tools. Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping. It provides for instance matrix operations, Principal Component Analysis, sparse linear supervised models, utility functions and more <doi:10.1093/bioinformatics/bty185>.
Maintained by Florian Privé. Last updated 7 months ago.
big-datalarge-matricesmemory-mapped-fileparallel-computingstatistical-methodsopenblascppopenmp
180 stars 10.59 score 394 scripts 16 dependents
pbreheny
biglasso:Extending Lasso Model Fitting to Big Data
Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing the user to analyze big data with limited RAM <doi:10.32614/RJ-2021-001>.
Maintained by Patrick Breheny. Last updated 24 days ago.
bigdatalassoout-of-coreparallel-computingcppopenmp
113 stars 9.84 score 74 scripts 1 dependents
vlarmet
cppRouting:Algorithms for Routing and Solving the Traffic Assignment Problem
Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://archive.siam.org/meetings/alenex05/papers/03agoldberg.pdf>), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>), new bidirectional A* (Pijls & Post (2009) <https://repub.eur.nl/pub/16100/ei2009-10.pdf>), Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) <doi:10.1007/978-3-540-68552-4_24>), PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) <doi:10.1016/j.jpdc.2012.02.007>). Algorithms for solving the traffic assignment problem are All-or-Nothing assignment, Method of Successive Averages, Frank-Wolfe algorithm (M. Fukushima (1984) <doi:10.1016/0191-2615(84)90029-8>), Conjugate and Bi-Conjugate Frank-Wolfe algorithms (M. Mitradjieva, P. O. Lindberg (2012) <doi:10.1287/trsc.1120.0409>), Algorithm-B (R. B. Dial (2006) <doi:10.1016/j.trb.2006.02.008>).
Maintained by Vincent Larmet. Last updated 12 days ago.
algorithmalgorithm-bbidirectional-a-star-algorithmc-plus-pluscontraction-hierarchiesdijkstra-algorithmdistancefrank-wolfeisochronesparallel-computingrcppshortest-pathstraffic-assignmentcpp
113 stars 7.72 score 39 scripts 4 dependents
acguidoum
Sim.DiffProc:Simulation of Diffusion Processes
It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.
Maintained by Arsalane Chouaib Guidoum. Last updated 1 years ago.
dynamic-systemmoment-equationsmonte-carlo-simulationparallel-computingstochastic-calculusstochastic-differential-equationtransition-density
13 stars 7.69 score 86 scripts 4 dependents
mihaiconstantin
parabar:Progress Bar for Parallel Tasks
A simple interface in the form of R6 classes for executing tasks in parallel, tracking their progress, and displaying accurate progress bars.
Maintained by Mihai Constantin. Last updated 3 months ago.
parallel-computingprogress-bar
20 stars 7.56 score 20 scripts 5 dependents
tlverse
delayed:A Framework for Parallelizing Dependent Tasks
Mechanisms to parallelize dependent tasks in a manner that optimizes the compute resources available. It provides access to "delayed" computations, which may be parallelized using futures. It is, to an extent, a facsimile of the 'Dask' library (<https://www.dask.org/>), for the 'Python' language.
Maintained by Jeremy Coyle. Last updated 11 months ago.
23 stars 7.03 score 39 scripts 8 dependents
uscbiostats
fmcmc:A friendly MCMC framework
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.
Maintained by George Vega Yon. Last updated 2 years ago.
adaptivebayesian-inferencemarkov-chain-monte-carlomcmcmetropolis-hastingsparallel-computing
16 stars 6.79 score 86 scripts 1 dependents
boennecd
parglm:Parallel GLM
Provides a parallel estimation method for generalized linear models without compiling with a multithreaded LAPACK or BLAS.
Maintained by Benjamin Christoffersen. Last updated 3 years ago.
generalized-linear-modelsparallel-computingopenblascpp
11 stars 6.41 score 39 scripts 4 dependents
mlr-org
rush:Rapid Parallel and Distributed Computing
Parallel computing with a network of local and remote workers. Fast exchange of results between the workers through a 'Redis' database. Key features include task queues, local caching, and sophisticated error handling.
Maintained by Marc Becker. Last updated 5 months ago.
11 stars 4.94 score 5 scripts
slzhang-fd
mirtjml:Joint Maximum Likelihood Estimation for High-Dimensional Item Factor Analysis
Provides constrained joint maximum likelihood estimation algorithms for item factor analysis (IFA) based on multidimensional item response theory models. So far, we provide functions for exploratory and confirmatory IFA based on the multidimensional two parameter logistic (M2PL) model for binary response data. Comparing with traditional estimation methods for IFA, the methods implemented in this package scale better to data with large numbers of respondents, items, and latent factors. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: 1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. <doi:10.1007/s11336-018-9646-5>; 2. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications. Journal of the American Statistical Association, <doi: 10.1080/01621459.2019.1635485>.
Maintained by Siliang Zhang. Last updated 4 years ago.
ifaitem-factor-analysislarge-scale-assessmentparallel-computingpsychometricsopenblascppopenmp
9 stars 4.21 score 12 scripts 1 dependents
christophergandrud
mcreplicate:Multi-Core Replicate
Multi-core replication function to make it easier to do fast Monte Carlo simulation. Based on the mcreplicate() function from the 'rethinking' package. The 'rethinking' package requires installing 'rstan', which is onerous to install, while also not adding capabilities to this function.
Maintained by Christopher Gandrud. Last updated 4 years ago.
5 stars 4.16 score 29 scripts
marcellgranat
currr:Apply Mapping Functions in Frequent Saving
Implementations of the family of map() functions with frequent saving of the intermediate results. The contained functions let you start the evaluation of the iterations where you stopped (reading the already evaluated ones from cache), and work with the currently evaluated iterations while remaining ones are running in a background job. Parallel computing is also easier with the workers parameter.
Maintained by Marcell Granat. Last updated 7 months ago.
checkpointsparallel-computingpurrr
21 stars 4.02 score 7 scriptsmihaiconstantin
doParabar:'foreach' Parallel Adapter for 'parabar' Backends
Provides a 'foreach' parallel adapter for 'parabar' backends. This package offers a minimal implementation of the '%dopar%' operator, enabling users to run 'foreach' loops in parallel, leveraging the parallel and progress-tracking capabilities of the 'parabar' package. Learn more about 'parabar' and 'doParabar' at <https://parabar.mihaiconstantin.com>.
Maintained by Mihai Constantin. Last updated 2 months ago.
1 stars 3.65 score 5 scripts 1 dependents
futureverse
future.tools:Tools for Working with Futures
Tools for Working with Futures.
Maintained by Henrik Bengtsson. Last updated 10 months ago.
parallel-computingparallel-programming
2 stars 2.60 score