Mapping Smoothed Odds Ratios from Individual-Level Data
Veronica Vieira, Scott Bartell, and Robin Bliss
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).
R (>= 2.10.0), gam, maptools, PBSmapping

Mapping Smoothed Effect Estimates from Individual-Level Data
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).

Lu Bai, Scott Bartell, Robin Bliss, and Veronica Vieira
Contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (submitted).  Includes convenient functions for mapping point estimates and confidence intervals, 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).