description

DESCRIPTION

@@ -6,8 +6,9 @@ Authors@R: c(person("Changwoo", "Lee", role=c("aut", "cre"), email="[email protected]
 Author: Changwoo Lee[aut, cre], Eun Sug Park[aut]
 Maintainer: Changwoo Lee <[email protected]>
 Description: Scalable methods for fitting Bayesian linear and generalized linear models in the presence of spatial exposure measurement error, represented as a multivariate normal prior distribution.
-             These models typically arises from two-stage Bayesian analysis of environmental exposures and health outcomes, where predictions of covariate of interest (exposure) from a first-stage model and their uncertainty information are used in a second-stage regression model. 
-             Related articles include Gryparis et al. (2009) (<https://doi.org/10.1093/biostatistics/kxn033>), Peng and Bell (2010) (<https://doi.org/10.1093/biostatistics/kxq017>), Chang, Peng, Dominici (2011) (<https://doi.org/10.1093/biostatistics/kxr002>), and Lee et al. (2024) (<https://arxiv.org/abs/2401.00634>). 
+             These models typically arises from a two-stage Bayesian analysis of environmental exposures and health outcomes.
+             From a first-stage model, predictions of the covariate of interest (exposure) and their uncertainty information (typically contained in MCMC samples) are used to form a multivariate normal prior distribution for exposure in a second-stage regression model. 
+             The package provides implementation of the methods used in Lee et al. (2024) <https://arxiv.org/abs/2401.00634>. 
 License: GPL (>= 3)
 Encoding: UTF-8
 LazyData: true

R/bglm_me.R

@@ -251,7 +251,6 @@ bglm_me <- function(Y,
     }
   }
   t_diff = difftime(Sys.time(), t_start, units = "secs")
-  #print(paste0("Exposure components contains negative vaules total ",sum(isnegative)," times among (# exposures) x n_y x (MCMC iter after burnin) = ",q," x ",n_y," x ",nsave*nthin," instances"))
 
   #### output ####
   out = list()

R/blm_me.R

@@ -239,7 +239,6 @@ blm_me <- function(Y,
     }
   }
   t_diff = difftime(Sys.time(), t_start, units = "secs")
-  #print(paste0("Exposure components contains negative vaules total ",sum(isnegative)," times among (# exposures) x n_y x (MCMC iter after burnin) = ",q," x ",n_y," x ",nsave*nthin," instances"))
 
   #### output ####
   out = list()