We present TNRKit.jl, 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.jl\cite{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.jl 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.jl，这是一个用于二维和三维经典统计模型及欧几里得晶格场论中张量网络重正化的开源Julia包。该包基于TensorKit.jl\cite{tensorkit}构建，提供了一个对称性感知框架，用于构建配分函数的张量网络表示，并采用TRG、HOTRG和LoopTNR等方法对其进行粗粒化。除了热力学量外，该包还能直接从不动点张量中提取通用共形数据——包括标度维度和中心荷。TNRKit.jl在设计上兼顾了易用性与可扩展性，为应用、基准测试和开发现代张量重正化算法提供了一个实用平台。本文同时作为张量网络重正化框架的自包含导论。