SparseM: a sparse matrix package for R. SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Howard, James P. II: Computational methods for numerical analysis with R (2017)
- Jhong, Jae-Hwan; Koo, Ja-Yong; Lee, Seong-Whan: Penalized B-spline estimator for regression functions using total variation penalty (2017)
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- De Livera, Alysha M.; Hyndman, Rob J.; Snyder, Ralph D.: Forecasting time series with complex seasonal patterns using exponential smoothing (2011)
- Patriota, Alexandre G.: A note on influence diagnostics in nonlinear mixed-effects elliptical models (2011)
- Wang, Huixia Judy; Hu, Jianhua: Identification of differential aberrations in multiple-sample array CGH studies (2011)
- Herrero, José R.; Navarro, Juan J.: Analysis of a sparse hypermatrix Cholesky with fixed-sized blocking (2007)
- Koenker, Roger; Ng, Pin: A Frisch-Newton algorithm for sparse quantile regression (2005)
- Koenker, Roger; Mizera, Ivan: Penalized triograms: total variation regularization for bivariate smoothing (2004)