We present TNRKit, an open-source Julia package for Tensor Network Renormalization (TNR) of two- and three-dimensional classical statistical models and Euclidean lattice field theories. Built on top of TensorKit, it provides a symmetry-aware framework for constructing tensor-network representations of partition functions and coarse-graining them using methods such as TRG, HOTRG, and LoopTNR. Beyond thermodynamic quantities, the package enables the extraction of universal conformal data -- including scaling dimensions and the central charge -- directly from fixed-point tensors. TNRKit is designed with both usability and extensibility in mind, offering a practical platform for applying, benchmarking, and developing modern tensor renormalization algorithms. This paper also serves as a self-contained introduction to the TNR framework.

翻译：我们介绍 TNRKit，一个用于二维和三维经典统计模型以及欧几里得格点场论张量网络重整化的开源 Julia 软件包。该软件包基于 TensorKit 构建，提供了一个对称性感知框架，用于构造配分函数的张量网络表示，并通过 TRG、HOTRG 和 LoopTNR 等方法对其进行粗粒化。除了热力学量外，该软件包还能直接从不动点张量中提取普适共形数据——包括标度维数和中心荷。TNRKit 在设计上兼顾易用性与可扩展性，为应用、基准测试和开发现代张量重整化算法提供了一个实用平台。本文同时可作为张量网络重整化框架的独立入门教程。