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The library used to construct a decision tree in R is recognized for its specialized functions that facilitate the creation and visualization of decision trees. This package allows users to build classification and regression trees, providing a straightforward interface for fitting models to data.

It includes methods for splitting nodes based on criteria like Gini impurity or mean squared error, which is essential in developing a decision-making framework in predictive modeling. Additionally, this library offers capabilities for plotting the generated trees, thereby aiding in the interpretation of the model.

The other libraries mentioned serve different purposes: one is focused on data visualization, another is geared towards creating a unified interface for various modeling techniques including decision trees, and the last one specializes in data manipulation. However, their functionalities do not directly pertain to the construction of decision trees as effectively as the chosen library does.