README.Rmd

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# ConTextNet

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The goal of ConTextNet is to discover influential text features from a corpus,
given an outcome of interest. These text features can be used to estimate causal
effects on the outcome using a generalization of the framework presented in
Fong & Grimmer (2016, 2021), or can be viewed as exploratory evidence to guide
confirmatory analyses where researchers design a small number of treatment
texts. To do this, ConTextNet walks users through building, training, tuning,
and interpreting a neural network with convolutional layers. ConTextNet relies
heavily on R Keras for modeling, and will likely require users to run at least
some operations on a high performance computing cluster (HPC) for corpora larger 
than a few thousand documents. The package will provide documentation and helper
functions to guide users through the necessary Python dependency installations
and to streamline interactions with common HPC interfaces.


## Installation

You can install the development version of ConTextNet like so:

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