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SparseM: A Sparse Matrix Package for R

Author

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  • Koenker, Roger
  • Ng, Pin
Abstract
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.

Suggested Citation

  • Koenker, Roger & Ng, Pin, 2003. "SparseM: A Sparse Matrix Package for R ," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i06).
    • Handle: RePEc:jss:jstsof:v:008:i06
    DOI: http://hdl.handle.net/10.18637/jss.v008.i06
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    Cited by:

    1. Berndt, Antje & Hollifield, Burton & Sandås, Patrik, 2014. "How Subprime Borrowers and Mortgage Brokers Shared the Pie," Working Paper Series 286, Sveriges Riksbank (Central Bank of Sweden).
    2. Hyndman, Rob J. & Lee, Alan J. & Wang, Earo, 2016. "Fast computation of reconciled forecasts for hierarchical and grouped time series," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 16-32.
    3. EnDer Su, 2021. "Testing stock market contagion properties between large and small stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 147-202, July.
    4. Ding Du & Pin Ng & Xiaobing Zhao, 2013. "Measuring currency exposure with quantile regression," Review of Quantitative Finance and Accounting, Springer, vol. 41(3), pages 549-566, October.
    5. Huixia Judy Wang & Jianhua Hu, 2011. "Identification of Differential Aberrations in Multiple-Sample Array CGH Studies," Biometrics, The International Biometric Society, vol. 67(2), pages 353-362, June.
    6. Luis Arturo Lopez, 2021. "Asymmetric information and personal affiliations in brokered housing transactions," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(2), pages 459-492, June.
    7. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    8. Bertho Tantular & Budi Nurani Ruchjana & Yudhie Andriyana & Anneleen Verhasselt, 2023. "Quantile Regression in Space-Time Varying Coefficient Model of Upper Respiratory Tract Infections Data," Mathematics, MDPI, vol. 11(4), pages 1-16, February.

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