We give novel Python and R interfaces for the (Java) Tetrad project for
causal modeling, search, and estimation. The Tetrad project is a mainstay in
the literature, having been under consistent development for over 30 years.
Some of its algorithms are now classics, like PC and FCI; others are recent
developments. It is increasingly the case, however, that researchers need to
access the underlying Java code from Python or R. Existing methods for doing
this are inadequate. We provide new, up-to-date methods using the JPype
Python-Java interface and the Reticulate Python-R interface, directly solving
these issues. With the addition of some simple tools and the provision of
working examples for both Python and R, using JPype and Reticulate to interface
Python and R with Tetrad is straightforward and intuitive.

我们为 Java 的 Tetrad 项目提供了新颖的 Python 和 R 接口，用于因果建模、搜索和估计。使用 JPype 和 Reticulate 将 Python 和 R 与 Tetrad 接口连接是直接解决这些问题的新方法。

Py-Tetrad 和 RPy-Tetrad: 适用于 Tetrad 因果搜索的具备 R 支持的新 Python 接口

Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for  Tetrad Causal Search

This paper evaluates the capability of two state-of-the-art artificial
intelligence (AI) models, GPT-3.5 and Bard, in generating Java code given a
function description. We sourced the descriptions from CodingBat.com, a popular
online platform that provides practice problems to learn programming. We
compared the Java code generated by both models based on correctness, verified
through the platform's own test cases. The results indicate clear differences
in the capabilities of the two models. GPT-3.5 demonstrated superior
performance, generating correct code for approximately 90.6% of the function
descriptions, whereas Bard produced correct code for 53.1% of the functions.
While both models exhibited strengths and weaknesses, these findings suggest
potential avenues for the development and refinement of more advanced
AI-assisted code generation tools. The study underlines the potential of AI in
automating and supporting aspects of software development, although further
research is required to fully realize this potential.

本研究评估了两种先进的人工智能模型 GPT-3.5 和 Bard 在给定函数描述时生成 Java 代码的能力，并发现 GPT-3.5 在正确生成代码方面的表现优于 Bard 约 37.5％。该研究强调了人工智能在软件开发中自动化和支持方面的潜力，但还需要进一步的研究来充分实现这一潜力。