软件专利


一、软件包开发

以下的R/Python软件包是我们课题组合作开发的!欢迎使用,并请引用对应的文献!

SpecificLDA

SpecificLDA:一个用于从文本数据中提取特定主题的Python包 

下载地址:https://github.com/XMU-Kuangnan-Fang-Team/SpecificLDA

论文: 国家治理政府注意力指数构建及其应用—基于新闻文本的测度. 统计研究. 2025

DescriptionSpecificLDA是一个基于定向LDA模型,用于从文本中提取指定主题信息,并构建注意力指数的Python包。该库提供了文本预处理、模型构建和趋势分析等功能。定向LDA模型融合了规则提取和传统LDA两类方法的优点。一方面,定向LDA模型基于传统LDA模型的思路构建而成,可以完全自动化地抽取定向LDA信息,将每篇文本的主题以概率分布的形式给出并根据主题进行聚类,能够克服规则提取法的主观性问题。另一方面,定向LDA模型借鉴了规则提取的思想,通过给定目标主题的先验词定向提取主题信息,且允许目标信息主题与其他信息主题使用不同的先验信息,可以克服传统LDA模型主题数难以确定和无法提取小主题等问题

GENetLib

GENetLib: A Python Library for Gene–environment Interaction Analysis via Deep Learning

下载地址:https://github.com/XMU-Kuangnan-Fang-Team/GENetLib

FunctanSNP

FunctanSNP: an R package for functional analysis of dense SNP data (with interactions)  

下载地址:https://CRAN.R-project.org/package=FunctanSNP.

论文:FunctanSNP: an R package for functional analysis of dense SNP data (with interactions)  . Bioinformatics. 2023

DescriptionDensely measured SNP data are routinely analyzed but face challenges due to its high dimensionality, especially when gene–environment interactions are incorporated. In recent literature, a functional analysis strategy has been developed, which treats dense SNP measurements as a realization of a genetic function and can ‘bypass’ the dimensionality challenge. However, there is a lack of portable and friendly software, which hinders practical utilization of these functional methods. We fill this knowledge gap and develop the R package FunctanSNP. This comprehensive package encompasses estimation, identification, and visualization tools and has undergone extensive testing using both simulated and real data, confirming its reliability. FunctanSNP can serve as a convenient and reliable tool for analyzing SNP and other densely measured data.

iSFun

iSFun: an R package for integrative dimension reduction analysis  

下载地址:https://CRAN.R-project.org/package=iSFun

论文: iSFun: an R Package for Integrative Dimension Reduction Analysis. Bioinformatics. 2022 

Description: In the analysis of high-dimensional omics data, dimension reduction techniques-including principal component analysis (PCA), partial least squares (PLS) and canonical correlation analysis (CCA)-have been extensively used. When there are multiple datasets generated by independent studies with compatible designs, integrative analysis has been developed and shown to outperform meta-analysis, other multidatasets analysis, and individual-data analysis. To facilitate integrative dimension reduction analysis in daily practice, we develop the R package iSFun, which can comprehensively conduct integrative sparse PCA, PLS and CCA, as well as meta-analysis and stacked analysis. The package can conduct analysis under the homogeneity and heterogeneity models and with the magnitude- and sign-based contrasted penalties. As a 'byproduct', this article is the first to develop integrative analysis built on the CCA technique, further expanding the scope of integrative analysis.

dSEIR

R package of dynamic SEIR model.  

下载地址: https://github.com/XMU-Kuangnan-Fang-Team/dSEIR

论文:1. 基于动态SEIR模型的传染性疾病预测及政策评估.管理科学学报. 2022   动态SEIR.pdf

              2. Effects of Public Health Interventions on Hospital Utilization in Patients with COVID-19:  a Comparative Study.  JMIR Public Health and Surveillance.  2020 

Description: For the prediction of the epidemic trend of COVID-19 pneumonia, a dynamic SEIR model was proposed by Kuangnan Fang , Rui Ten et al., which is based on the Susceptible-Exposed-Infected-Removed model of transmission dynamics. The advantages of the model are as follows :(1) the model parameters can be dynamically estimated based on the real-time updated data; (2) compared with the traditional SEIR model, which assumes a closed environment, the proposed model can take into account the impact of population flow on disease transmission and has a better prediction effect.

RDA

R pcakge for the book 《R数据分析-方法与案例详解》

下载地址:https://github.com/ruiqwy/RDA

引用 文献:方匡南、朱建平、姜叶飞. R数据分析——方法与案例详解. 电子工业出版社. 2015.2

 京东: http://item.jd.com/11652652.html 


(二) 软件培训课程

  1. R数据分析(人大经济论坛主办) 现场班 http://bbs.pinggu.org/thread-3820540-1-1.html

  2. R语言视频课程 初级班  http://baoming.pinggu.org/Default.aspx?id=24

  3. R语言视频课程 高级班  http://baoming.pinggu.org/Default.aspx?id=27

 

二、发明专利与技术标准

(一)发明专利

  1. 一种车险出险预测方法及计算机设备. CN202210567932.9

  2. 一种基于多种无监督方法融合的单指标异常检测方法. CN202010200442.6.

  3. 一种基于统计学习的液晶面板CF图片识别方法. CN201910450350.0.

  4. 经济景气指数检测方法、电子设备、储存介质. CN201810654477.X


(二)技术标准

  1.   中小微企业金融借贷信用评价指南.团体标准(标准编号:T/ZAITS 20201—2022).