Matrix

R package Matrix: Sparse and Dense Matrix Classes and Methods , Classes and methods for dense and sparse matrices and operations on them using Lapack and SuiteSparse. (Source: http://cran.r-project.org/web/packages)

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References in zbMATH (referenced in 74 articles )

Showing results 1 to 20 of 74.
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  1. Kharrat, Tarak; McHale, Ian G.; Peña, Javier López: Plus-minus player ratings for soccer (2020)
  2. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  3. Christophe Ambroise, Alia Dehman, Pierre Neuvial, Guillem Rigaill, Nathalie Vialaneix: Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics (2019) arXiv
  4. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  5. David Smith; Malcolm Faddy: Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data (2019) not zbMATH
  6. Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
  7. N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz: Randomized Matrix Decompositions Using R (2019) not zbMATH
  8. Suman Rakshit; Adrian Baddeley; Gopalan Nair: Efficient Code for Second Order Analysis of Events on a Linear Network (2019) not zbMATH
  9. Yuqing Pan, Qing Mai, Xin Zhang: TULIP: A Toolbox for Linear Discriminant Analysis with Penalties (2019) arXiv
  10. Embrechts, P.; Kirchner, M.: Hawkes graphs (2018)
  11. Fenn, R. and Wenzel, A.: Quandle and Biquandle Homology Calculation in R (2018) not zbMATH
  12. Hofert, Marius; Kojadinovic, Ivan; Mächler, Martin; Yan, Jun: Elements of copula modeling with R (2018)
  13. John Hughes: sklarsomega: An R Package for Measuring Agreement Using Sklar's Omega Coefficient (2018) arXiv
  14. Marie Perrot-Dockès, Céline Lévy-Leduc, Loïc Rajjou: Estimation of large block structured covariance matrices: Application to ”multi-omic” approaches to study seed quality (2018) arXiv
  15. Perrot-Dockès, Marie; Lévy-Leduc, Céline; Chiquet, Julien; Sansonnet, Laure; Brégère, Margaux; Étienne, Marie-Pierre; Robin, Stéphane; Genta-Jouve, Grégory: A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data (2018)
  16. Suesse, Thomas: Estimation of spatial autoregressive models with measurement error for large data sets (2018)
  17. Suesse, Thomas: Marginal maximum likelihood estimation of SAR models with missing data (2018)
  18. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  19. Amatya, Anup; Demirtas, Hakan: PoisNor: an R package for generation of multivariate data with Poisson and normal marginals (2017)
  20. Andrew Zammit-Mangion, Noel Cressie: FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (2017) arXiv

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