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README.md

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 ## DeepGenomeScan : A Deep Learning Approach for Whole Genome Scan (WGS) and Genome-wide Association Studies (GWAS)
 
-  This package implements the genome scan and genome-wide association studies using deep neural networks (i.e, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN)). DeepGenomeScan offers heuristic computational framework integrating different neural network architectures (i.e.,Multi-Layer Perceptron (MLP), convolutional neural network(CNN)) and robust resampling  methods, as well as the Model-Agnostic interpretation of feature importance for convolutional neural networks. DeepGenomeScan, in other words, deep learning for genome-wide scanning, is a deep learning approach for detecting signatures of natural selection and for performing various omics-based genome-wide association studies, such as GWAS, PWAS, TWAS, MWAS. The design makes the implemention user-friendly. It is compatible with most self-defined machine learning models (the self-defined models shuold be complete, including tunable parameters, fitted model, predicted model, examples can be found in our tutorial). Users can adopt the package's framework to study various ecological and evolutionary problems.
+  This package implements the genome scan and genome-wide association studies using deep neural networks (i.e, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN)). DeepGenomeScan offers heuristic computational framework integrating different neural network architectures (i.e.,Multi-Layer Perceptron (MLP), convolutional neural network(CNN)) and robust resampling  methods, as well as the Model-Agnostic interpretation of feature importance for convolutional neural networks. DeepGenomeScan, in other words, deep learning for genome-wide scanning, is a deep learning approach for detecting signatures of natural selection and for performing various omics-based genome-wide association studies, such as GWAS, PWAS, TWAS, MWAS. The design makes the implemention user-friendly. It is compatible with most self-defined machine learning models (the self-defined models shuold be complete, including tunable parameters, fitted model, predicted model, examples can be found in our tutorial). Users can adopt the package's framework to study various ecological and evolutionary questions.
 
 ## Install packages
 `````{r}