There are several popular libraries in R that are commonly used for artificial intelligence (AI) and machine learning (ML) tasks in finance. Here are a few libraries you can explore: caret (Classification And REgression Training): A comprehensive library that provides a unified interface for training and evaluating ML models. It supports various algorithms, feature selection, cross-validation, and model tuning. xgboost and lightgbm: These libraries offer implementations of gradient boosting algorithms, which are powerful for regression and classification tasks. They are known for their efficiency and high performance. randomForest and ranger: These libraries provide implementations of random forest algorithms, which are ensemble learning methods that use multiple decision trees for classification and regression. nnet and neuralnet: These libraries allow you to create and train neural networks for various finance-related tasks, such as predicting stock prices or credit risk assessment. TensorFlow and Keras: These libraries enable deep learning and neural network modeling. Although they are primarily associated with Python, there are R interfaces available that allow you to work with these libraries in R. quantmod: This library focuses on quantitative financial modeling and analysis. It provides tools for retrieving and analyzing financial data, as well as implementing technical analysis indicators.