spam: SPArse Matrix: Set of functions for sparse matrix algebra. Differences with SparseM/Matrix are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within GMRF. (4) S3 and S4 like-”compatible” ... and it is fast.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Bevilacqua, M.; Fassò, A.; Gaetan, C.; Porcu, E.; Velandia, D.: Covariance tapering for multivariate Gaussian random fields estimation (2016)
- Furrer, Reinhard; Bachoc, François; Du, Juan: Asymptotic properties of multivariate tapering for estimation and prediction (2016)
- Vepakomma, Praneeth; Elgammal, Ahmed: A fast algorithm for manifold learning by posing it as a symmetric diagonally dominant linear system (2016)
- Bevilacqua, Moreno; Gaetan, Carlo: Comparing composite likelihood methods based on pairs for spatial Gaussian random fields (2015)
- Hirano, Toshihiro; Yajima, Yoshihiro: Covariance tapering for prediction of large spatial data sets in transformed random fields (2013)
- Furrer, Reinhard; Genton, Marc G.: Aggregation-cokriging for highly multivariate spatial data (2011)
- Greasby, Tamara A.; Sain, Stephan R.: Multivariate spatial analysis of climate change projections (2011)
- Kaufman, Cari G.; Bingham, Derek; Habib, Salman; Heitmann, Katrin; Frieman, Joshua A.: Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology (2011)
- Sain, Stephan R.; Furrer, Reinhard; Cressie, Noel: A spatial analysis of multivariate output from regional climate models (2011)
- Cooley, Daniel; Sain, Stephan R.: Spatial hierarchical modeling of precipitation extremes from a regional climate model (2010)
- Schliep, Erin M.; Cooley, Daniel; Sain, Stephen R.; Hoeting, Jennifer A.: A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling (2010)