Packaged: 2019-04-17 13:12:24 UTC; lauren.hanna

Packaged: 2019-05-15 19:18:08 UTC; lauren.hanna

Packaged: 2020-01-24 18:31:27 UTC; lauren.hanna

Package: MapGAM
Type: Package
Title: Mapping Smoothed Odds Ratios from Individual-Level Data
Version: 0.6-1
Date: 2012-12-23
Author: Veronica Vieira, Scott Bartell, and Robin Bliss
Maintainer: Scott Bartell <sbartell@uci.edu>
Description: Contains functions for mapping risk surfaces using
individual-level data such as case-control study data, using
generalized additive models (GAMs) to smooth over
two-dimensional location while adjusting for any confounding
variables.  Includes convenient functions for mapping and
permutation tests for the null hypothesis that spatial location
is not associated with risk (adjusting for confounders).
License: GPL-3
Depends: R (>= 2.10.0), gam, maptools, PBSmapping
Suggests: maps, mapproj
Packaged: 2012-12-24 01:20:28 UTC; sbartell
Repository: CRAN
Date/Publication: 2012-12-24 07:12:14

Package: MapGAM
Type: Package
Title: Mapping Smoothed Odds Ratios from Individual-Level Data
Version: 0.6
Date: 2012-12-18
Author: Veronica Vieira, Scott Bartell, and Robin Bliss
Maintainer: Scott Bartell <sbartell@uci.edu>
Description: Contains functions for mapping risk surfaces using
individual-level data such as case-control study data, using
generalized additive models (GAMs) to smooth over
two-dimensional location while adjusting for any confounding
variables.  Includes convenient functions for mapping and
permutation tests for the null hypothesis that spatial location
is not associated with risk (adjusting for confounders).
License: GPL-3
Depends: R (>= 2.10.0), gam, maptools, PBSmapping
Suggests: maps, mapproj
Packaged: 2012-12-18 08:41:59 UTC; sbartell
Repository: CRAN
Date/Publication: 2012-12-18 14:46:25

Title: Mapping Smoothed Effect Estimates from Individual-Level Data
Description: Contains functions for mapping odds ratios or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting for confounding variables, using methods described by Kelsall and Diggle (1998) and Webster at al. (2006).  Includes convenient functions for mapping, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).