eflm-package: eflm: Efficient Fitting of Linear and Generalized Linear...
In pachamaltese/eflm: Efficient Fitting of Linear and Generalized Linear Models
eflm-package R Documentation
eflm: Efficient Fitting of Linear and Generalized Linear Models
Description
Efficient Fitting of Linear and Generalized Linear Models by using just base R. As an alternative to lm() and glm(), this package provides elm() and eglm(), with a significant speedup when the number of observations is larger than the number of parameters to estimate. The speed gains are obtained by reducing the NxP model matrix to a PxP matrix, and the best computational performance is obtained when R is linked against OpenBLAS, Intel MKL or other optimized BLAS library. This implementation aims at being compatible with 'broom' and 'sandwich' packages for summary statistics and clustering by providing S3 methods.
Author(s)
Maintainer: Mauricio Vargas mavargas11@uc.cl (ORCID)
Other contributors:
Alexey Kravchenko [thesis advisor]
Constanza Prado [thesis advisor]
Yoto Yotov [thesis advisor]
Neal Fulz (independent code auditing) [reviewer]
Rahul Saxena (improved broom integration) [contributor]
The United Nations [data contributor, copyright holder, funder]
See Also
Useful links:
pachamaltese/eflm documentation built on July 27, 2024, 12:27 p.m.
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| eflm-package | R Documentation |
eflm: Efficient Fitting of Linear and Generalized Linear Models
Description
Efficient Fitting of Linear and Generalized Linear Models by using just base R. As an alternative to lm() and glm(), this package provides elm() and eglm(), with a significant speedup when the number of observations is larger than the number of parameters to estimate. The speed gains are obtained by reducing the NxP model matrix to a PxP matrix, and the best computational performance is obtained when R is linked against OpenBLAS, Intel MKL or other optimized BLAS library. This implementation aims at being compatible with 'broom' and 'sandwich' packages for summary statistics and clustering by providing S3 methods.
Author(s)
Maintainer: Mauricio Vargas mavargas11@uc.cl (ORCID)
Other contributors:
Alexey Kravchenko [thesis advisor]
Constanza Prado [thesis advisor]
Yoto Yotov [thesis advisor]
Neal Fulz (independent code auditing) [reviewer]
Rahul Saxena (improved broom integration) [contributor]
The United Nations [data contributor, copyright holder, funder]
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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