readme

README.Rmd

@@ -26,17 +26,14 @@ See a vignette with NO2 exposure data: [[link]](https://changwoo-lee.github.io/b
 
 See bspme_1.0.0.pdf for the pdf file of the package manual.
 
-**bspme** is an R package that provides a set of functions for **B**ayesian **sp**atial exposure **m**easurement **e**rror models, the Bayesian linear and generalized linear models with the presence of spatially correlated measurement error of covariate(s). For more details, please see the following paper:
-
-> Lee, C. J., Symanski, E., Rammah, A., Kang, D. H., Hopke, P. K., & Park, E. S. (2024). A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology. arXiv preprint arXiv:2401.00634. <https://arxiv.org/abs/2401.00634>
-
-
-The **bspme** package provides fast, scalable inference tools for Bayesian linear and generalized linear models with spatial exposure measurement error. 
+**bspme** is an R package that provides fast, scalable inference tools for **B**ayesian **sp**atial exposure **m**easurement **e**rror models, the Bayesian linear and generalized linear models with the presence of spatially correlated measurement error of covariate(s). 
 These models typically arise 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 $X\sim N(\mu, \Sigma)$ for exposure in a second-stage regression model. 
 Naive, non-sparse choices of the precision matrix $Q = \Sigma^{-1}$ of the multivariate normal (such as a sample precision matrix) leads to the MCMC posterior inference algorithm infeasible to run for a large number of subjects $n$ because of the cubic computational cost associated with the $n$-dimensional MVN prior.
-With the sparse precision matrix $Q$ obtained from the Vecchia approximation, the **bspme** package offers a fast, scalable algorithm to conduct posterior inference for large health datasets, with the number of subjects $n$ possibly reaching tens of thousands.
+With a sparse precision matrix $Q$ obtained from the Vecchia approximation, the **bspme** package offers fast, scalable algorithms to conduct posterior inference with large health datasets, with the number of subjects $n$ possibly reaching tens of thousands.
+ For more details, please see the following paper:
 
+> Lee, C. J., Symanski, E., Rammah, A., Kang, D. H., Hopke, P. K., & Park, E. S. (2024). A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology. arXiv preprint arXiv:2401.00634. <https://arxiv.org/abs/2401.00634>
 ## Installation
 
 You can install the development version of bspme with the following code:

README.md

@@ -16,36 +16,31 @@ See a vignette with NO2 exposure data:
 
 See bspme_1.0.0.pdf for the pdf file of the package manual.
 
-**bspme** is an R package that provides a set of functions for
-**B**ayesian **sp**atial exposure **m**easurement **e**rror models, the
-Bayesian linear and generalized linear models with the presence of
-spatially correlated measurement error of covariate(s). For more
-details, please see the following paper:
+**bspme** is an R package that provides fast, scalable inference tools
+for **B**ayesian **sp**atial exposure **m**easurement **e**rror models,
+the Bayesian linear and generalized linear models with the presence of
+spatially correlated measurement error of covariate(s). These models
+typically arise 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 $X\sim N(\mu, \Sigma)$ for exposure in a
+second-stage regression model. Naive, non-sparse choices of the
+precision matrix $Q = \Sigma^{-1}$ of the multivariate normal (such as a
+sample precision matrix) leads to the MCMC posterior inference algorithm
+infeasible to run for a large number of subjects $n$ because of the
+cubic computational cost associated with the $n$-dimensional MVN prior.
+With a sparse precision matrix $Q$ obtained from the Vecchia
+approximation, the **bspme** package offers fast, scalable algorithms to
+conduct posterior inference with large health datasets, with the number
+of subjects $n$ possibly reaching tens of thousands. For more details,
+please see the following paper:
 
 > Lee, C. J., Symanski, E., Rammah, A., Kang, D. H., Hopke, P. K., &
 > Park, E. S. (2024). A scalable two-stage Bayesian approach accounting
 > for exposure measurement error in environmental epidemiology. arXiv
-> preprint arXiv:2401.00634. <https://arxiv.org/abs/2401.00634>
-
-The **bspme** package provides fast, scalable inference tools for
-Bayesian linear and generalized linear models with spatial exposure
-measurement error. These models typically arise 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
-$X\sim N(\mu, \Sigma)$ for exposure in a second-stage regression model.
-Naive, non-sparse choices of the precision matrix $Q = \Sigma^{-1}$ of
-the multivariate normal (such as a sample precision matrix) leads to the
-MCMC posterior inference algorithm infeasible to run for a large number
-of subjects $n$ because of the cubic computational cost associated with
-the $n$-dimensional MVN prior. With the sparse precision matrix $Q$
-obtained from the Vecchia approximation, the **bspme** package offers a
-fast, scalable algorithm to conduct posterior inference for large health
-datasets, with the number of subjects $n$ possibly reaching tens of
-thousands.
-
-## Installation
+> preprint arXiv:2401.00634. <https://arxiv.org/abs/2401.00634> \##
+> Installation
 
 You can install the development version of bspme with the following
 code: