Brownian motion and fractional Brownian motion have been widely applied in statistical modeling in finance, telecommunication, network traffic, neuroscience, physics, and other fields. More realistic models for real time series data, such as multifractional processes, generalize these classical models by allowing their regularity to vary over time. A new class of Gaussian Haar-based multifractional processes, which utilizes the Haar wavelet series representation, was recently introduced. It significantly extends the range of available models by incorporating more general classes of Hurst functions. The Rmfrac package was developed to simulate multifractional time series. The package also comprises several functions for the analysis and visualization of time series. It includes the estimation of the Hurst function and local fractal dimension, clustering realizations and computing various geometric statistics of these time series. The package also offers a Shiny application to visualize simulation and estimation results. The article presents an overview of the Rmfrac package and exemplifies its main functionalities.

翻译：布朗运动和分数布朗运动已广泛应用于金融、电信、网络流量、神经科学、物理学等领域的统计建模。更符合真实时间序列数据的模型（如多重分形过程）通过允许其正则性随时间变化，对这些经典模型进行了推广。最近引入的一类基于Haar高斯过程的新多重分形模型，利用Haar小波级数表示，通过纳入更广义的Hurst函数族，显著扩展了可用模型的范围。Rmfrac包专为模拟多重分形时间序列而开发。该包还包含多个用于时间序列分析与可视化的函数，包括Hurst函数和局部分形维数的估计、序列聚类以及这些时间序列各种几何统计量的计算。此外，该包还提供了一个Shiny应用，用于可视化模拟与估计结果。本文概述了Rmfrac包的主要功能并展示了其典型应用。