References in zbMATH (referenced in 14 articles )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Bordes, Laurent; Chauveau, Didier: Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data (2016)
  2. González, Miguel; Gutiérrez, Cristina; Martínez, Rodrigo; Minuesa, Carmen; del Puerto, Inés M.: Bayesian analysis for controlled branching processes (2016)
  3. Kustosz, Christoph P.; Leucht, Anne; Müller, Christine H.: Tests based on simplicial depth for AR(1) models with explosion (2016)
  4. Marius Hofert; Martin Mächler: Parallel and Other Simulations in R Made Easy: An End-to-End Study (2016)
  5. Teisseyre, Paweł; Kłopotek, Robert A.; Mielniczuk, Jan: Random subspace method for high-dimensional regression with the R package regRSM (2016)
  6. Bernd Bischl; Michel Lang; Olaf Mersmann; Jörg Rahnenführer; Claus Weihs: BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments (2015)
  7. Christopher Paciorek; Benjamin Lipshitz; Wei Zhuo; Prabhat; Cari G. Kaufman; Rollin Thomas: Parallelizing Gaussian Process Calculations in R (2015)
  8. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  9. Michael Kane; John Emerson; Stephen Weston: Scalable Strategies for Computing with Massive Data (2013)
  10. Nagarajan, Radhakrishnan; Scutari, Marco; Lèbre, Sophie: Bayesian networks in R. With applications in systems biology (2013)
  11. Nguyen, P.; Brown, P.E.; Stafford, J.: Mapping cancer risk in southwestern Ontario with changing census boundaries (2012)
  12. Stefan Theußl; Ingo Feinerer; Kurt Hornik: A tm Plug-In for Distributed Text Mining in R (2012)
  13. Yamamoto, Yoshikazu; Nakano, Junji; Fujiwara, Takeshi: Parallel computing in the statistical system Jasp (2010)
  14. Tierney, Luke; Rossini, A.J.; Li, Na: Snow: A parallel computing framework for the R system (2009)