Documentation update

Author: stmcg

DESCRIPTION

@@ -9,14 +9,13 @@ Authors@R: c(person("Sean", "McGrath", role = c("aut", "cre"),
                       email = "cenhao.zhu@gmail.com"),
               person("Rui", "Duan", role = c("aut"), 
                       email = "rduan@hsph.harvard.edu"))
-Maintainer: Sean McGrath <sean_mcgrath@g.harvard.edu>
-Description: Implements methods to estimate a low rank matrix in an 
-    underrepresented target population from multi-source data. These methods 
-    leverage similarity in the latent row and column spaces between the source 
-    and target populations to improve estimation in the target population. 
-    The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) 
-    method and the direct projection LEARNER (D-LEARNER) method described
-    by McGrath et al. (2024) <doi:10.48550/arXiv.2412.20605>.
+Maintainer: Sean McGrath <sean.mcgrath514@gmail.com>
+Description: Implements transfer learning methods for low-rank matrix 
+    estimation. These methods leverage similarity in the latent row and column 
+    spaces between the source and target populations to improve estimation in 
+    the target population. The methods include the LatEnt spAce-based tRaNsfer 
+    lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) 
+    method described by McGrath et al. (2024) <doi:10.48550/arXiv.2412.20605>.
 License: GPL (>=3)
 Encoding: UTF-8
 LazyData: true

README.Rmd

@@ -19,7 +19,7 @@ knitr::opts_chunk$set(
 
 # learner
 
-The `learner` package implements methods to estimate a low rank matrix in an underrepresented target population from multi-source data. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by [McGrath et al. (2024)](https://doi.org/10.48550/arXiv.2412.20605).
+The `learner` package implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by [McGrath et al. (2024)](https://doi.org/10.48550/arXiv.2412.20605).
 
 ## Installation
 
@@ -37,7 +37,7 @@ We illustrate an example of how `learner` can be used. We first load the package
 library(learner)
 ```
 
-In this illustration, we will use one of the toy data sets in the package (`dat_highsim`) that has a high degree of similarity between the latent spaces of the source and target populations. The object `dat_highsim` is a list which contains the observed source population data matrix `Y_source` and the target population data matrix `Y_target`. As these data were simulated, the true values of the matrices are stored in `dat_highsim` (see `Theta_source` and `Theta_target`).
+In this illustration, we will use one of the toy data sets in the package (`dat_highsim`) that has a high degree of similarity between the latent spaces of the source and target populations. The object `dat_highsim` is a list which contains the observed source population data matrix `Y_source` and the target population data matrix `Y_target`. Since the data was simulated, the true values of the matrices are included in `dat_highsim` as `Theta_source` and `Theta_target`.
 
 #### LEARNER Method
 

README.md

@@ -9,13 +9,12 @@ coverage](https://codecov.io/gh/stmcg/learner/graph/badge.svg)](https://app.code
 
 # learner
 
-The `learner` package implements methods to estimate a low rank matrix
-in an underrepresented target population from multi-source data. These
-methods leverage similarity in the latent row and column spaces between
-the source and target populations to improve estimation in the target
-population. The methods include the LatEnt spAce-based tRaNsfer lEaRning
-(LEARNER) method and the direct projection LEARNER (D-LEARNER) method
-described by [McGrath et
+The `learner` package implements transfer learning methods for low-rank
+matrix estimation. These methods leverage similarity in the latent row
+and column spaces between the source and target populations to improve
+estimation in the target population. The methods include the LatEnt
+spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection
+LEARNER (D-LEARNER) method described by [McGrath et
 al. (2024)](https://doi.org/10.48550/arXiv.2412.20605).
 
 ## Installation
@@ -42,8 +41,8 @@ package (`dat_highsim`) that has a high degree of similarity between the
 latent spaces of the source and target populations. The object
 `dat_highsim` is a list which contains the observed source population
 data matrix `Y_source` and the target population data matrix `Y_target`.
-As these data were simulated, the true values of the matrices are stored
-in `dat_highsim` (see `Theta_source` and `Theta_target`).
+Since the data was simulated, the true values of the matrices are
+included in `dat_highsim` as `Theta_source` and `Theta_target`.
 
 #### LEARNER Method