diff --git a/R/bglm_me.R b/R/bglm_me.R
index 48fa0d7..0021a13 100644
--- a/R/bglm_me.R
+++ b/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.
diff --git a/R/blm_me.R b/R/blm_me.R
index 3e1c309..8d9cc72 100644
--- a/R/blm_me.R
+++ b/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.
diff --git a/R/bspme-package.R b/R/bspme-package.R
index d1fbb0a..0408c32 100644
--- a/R/bspme-package.R
+++ b/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
 
diff --git a/bspme_1.0.1.pdf b/bspme_1.0.1.pdf
index 967c064..2c91986 100644
Binary files a/bspme_1.0.1.pdf and b/bspme_1.0.1.pdf differ
diff --git a/man/NO2_Jan2012.Rd b/man/NO2_Jan2012.Rd
index f952df7..de9fb30 100644
--- a/man/NO2_Jan2012.Rd
+++ b/man/NO2_Jan2012.Rd
@@ -10,7 +10,7 @@ A data frame with 651 (21 sites x 31 days) rows and 5 variables:
 \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)}
 }
 }
 \usage{
diff --git a/man/bglm_me.Rd b/man/bglm_me.Rd
index ad85435..8283e12 100644
--- a/man/bglm_me.Rd
+++ b/man/bglm_me.Rd
@@ -51,7 +51,7 @@ This function fits a Bayesian generalized linear model in the presence of spatia
 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.
 }
 \details{
 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,
diff --git a/man/blm_me.Rd b/man/blm_me.Rd
index fd527a0..140fdb7 100644
--- a/man/blm_me.Rd
+++ b/man/blm_me.Rd
@@ -46,7 +46,7 @@ list of the following:
 \description{
 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.
 }
 \details{
 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,