ver 1.0.1 update 2

R/bglm_me.R

@@ -4,7 +4,7 @@
 #' One of the most important features of this function is that it allows a sparse matrix input for the prior precision matrix of \eqn{X} for scalable computation.
 #' As of version 1.0.0, only the Bayesian logistic regression model is supported among GLMs, and
 #' function \code{bglm_me()} runs a Gibbs sampler to carry out posterior inference using Polya-Gamma augmentation (Polson et al., 2013).
-#' See the below "Details" section below for the model description and Lee et al. (2024) for an application example in environmental epidemiology.
+#' See the "Details" section below for the model description and Lee et al. (2024) for an application example in environmental epidemiology.
 #'
 #' Let \eqn{Y_i} be a binary response, \eqn{X_i} be a \eqn{q\times 1} covariate vector that is subject to spatial exposure measurement error,
 #' and \eqn{Z_i} be a \eqn{p\times 1} covariate vector without measurement error.

R/blm_me.R

@@ -2,7 +2,7 @@
 #'
 #' This function fits a Bayesian linear regression model in the presence of spatial exposure measurement error for covariate(s) \eqn{X}.
 #' One of the most important features of this function is that it allows a sparse matrix input for the prior precision matrix of \eqn{X} for scalable computation.
-#' Function \code{blm_me()} runs a Gibbs sampler to carry out posterior inference;  see the below "Details" section below for the model description, and Lee et al. (2024) for an application example in environmental epidemiology.
+#' Function \code{blm_me()} runs a Gibbs sampler to carry out posterior inference;  see the "Details" section below for the model description, and Lee et al. (2024) for an application example in environmental epidemiology.
 #'
 #' Let \eqn{Y_i} be a continuous response, \eqn{X_i} be a \eqn{q\times 1} covariate vector that is subject to spatial exposure measurement error,
 #' and \eqn{Z_i} be a \eqn{p\times 1} covariate vector without measurement error.

R/bspme-package.R

@@ -20,7 +20,7 @@ NULL
 #'   \item{site_name}{monitoring station name}
 #'   \item{lon}{monitoring station longitude}
 #'   \item{lat}{monitoring station latitude}
-#'   \item{lnNO2}{natural logarithm of daily average NO2 concentrations, measured in parts per billion by volume (ppbv)}
+#'   \item{lnNO2}{natural logarithm of daily average NO2 concentrations measured in parts per billion by volume (ppbv)}
 #' }
 NULL