Fix class name in structure

README.Rmd

@@ -25,7 +25,7 @@ Cesaire J. K. Fouodo
 ### Introduction
 Recent technological advances have enabled the simultaneous targeting of multiple pathways to enhance therapies for complex diseases. This often results in the collection of numerous data entities across various layers of patient groups, posing a challenge for integrating all data into a single analysis. Ideally, data of the different layers are measured in the same individuals, allowing for early or intermediate integrative techniques. However, these techniques are challenging when patient data only partially overlap. Additionally, the internal structure of each data entity may necessitate specific statistical methods rather than applying the same method across all layers. Late integration modeling addresses this by analyzing each data entity separately to obtain layer-specific results, which are then integrated using aggregation methods. Currently, no R package offers this flexibility.
 
-We introduce the fuseMLR package for late integration prediction modeling in R. This package allows users to define studies with multiple layers, data entities, and layer-specific machine learning methods. FuseMLR is user-friendly, enables training of different models across layers and automatically performs aggregation once layer-specific training is completed. Additionally, fuseMLR allows for variable selection at the layer level and makes predictions for new data entities.
+We introduce the fuseMLR package for late integration prediction modeling in R. This package allows users to define studies with multiple layers, data entities, and layer-specific machine learning methods. The package fuseMLR is user-friendly, enables training of different models across layers and automatically performs aggregation once layer-specific training is completed. Additionally, fuseMLR allows for variable selection at the layer level and makes predictions for new data entities.
 
 `fuseMLR` is an object-oriented package based on `R6` version 2.5.1. Refer to our [cheat sheet](https://github.com/imbs-hl/fuseMLR/blob/master/README_files/figure-gfm/fusemlrcheatsheet.pdf) for a quick overview of classes and functionalities.
 

README.md

@@ -33,9 +33,9 @@ flexibility.
 
 We introduce the fuseMLR package for late integration prediction
 modeling in R. This package allows users to define studies with multiple
-layers, data entities, and layer-specific machine learning methods.
-FuseMLR is user-friendly, enables training of different models across
-layers and automatically performs aggregation once layer-specific
+layers, data entities, and layer-specific machine learning methods. The
+package fuseMLR is user-friendly, enables training of different models
+across layers and automatically performs aggregation once layer-specific
 training is completed. Additionally, fuseMLR allows for variable
 selection at the layer level and makes predictions for new data
 entities.