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：我们提出 TNRKit，一个用于二维和三维经典统计模型及欧几里得格点场论张量网络重整化(TNR)的开源 Julia 包。该软件包构建于 TensorKit 之上，提供了一种具有对称性感知能力的框架，用于构造配分函数的张量网络表示，并利用 TRG、HOTRG 和 LoopTNR 等方法对其进行粗粒化处理。除了热力学量之外，该软件包还支持直接从不动点张量中提取普适共形场数据——包括标度维数和中心荷。TNRKit 在设计中兼顾了易用性和可扩展性，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。本文同时也可作为 TNR 框架的独立入门指南。