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SpatialVS

Spatial Variable Selection

v1.1 · Nov 9, 2018 · GPL-2

Description

Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.

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CRAN incoming feasibility

Maintainer: ‘Yili Hong <yilihong@vt.edu>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Yili",
                      family = "Hong",
                      role = c("aut", "cre"),
                      email = "yilihong@vt.edu"),
               person(given = "Li",
                      family = "Xu",
                      role = "aut"),
               person(given = "Yimeng",
                      family = "Xie",
                      role = "aut"),
               person(given = "Zhongnan",
                      family = "Jin",
                      role = "aut"))
as necessary.

The Description field contains
  al. (2018, <arXiv:1809.06418>). The package also contains the Lyme
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Yili Hong <yilihong@vt.edu>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Yili",
                      family = "Hong",
                      role = c("aut", "cre"),
                      email = "yilihong@vt.edu"),
               person(given = "Li",
                      family = "Xu",
                      role = "aut"),
               person(given = "Yimeng",
                      family = "Xie",
                      role = "aut"),
               person(given = "Zhongnan",
                      family = "Jin",
                      role = "aut"))
as necessary.

The Description field contains
  al. (2018, <arXiv:1809.06418>). The package also contains the Lyme
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-devel-linux-x86_64-debian-clang

CRAN incoming feasibility

Maintainer: ‘Yili Hong <yilihong@vt.edu>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Yili",
                      family = "Hong",
                      role = c("aut", "cre"),
                      e
...[truncated]...
amily = "Jin",
                      role = "aut"))
as necessary.

The Description field contains
  al. (2018, <arXiv:1809.06418>). The package also contains the Lyme
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Yili Hong <yilihong@vt.edu>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Yili",
                      family = "Hong",
                      role = c("aut", "cre"),
                      e
...[truncated]...
amily = "Jin",
                      role = "aut"))
as necessary.

The Description field contains
  al. (2018, <arXiv:1809.06418>). The package also contains the Lyme
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Dependency Network

Dependencies Reverse dependencies MASS nlme fields SpatialVS

Version History

new 1.1 Mar 10, 2026
updated 1.1 ← 1.0 diff Nov 9, 2018
new 1.0 Sep 29, 2018