方匡南的个人网站

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

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

Densely 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

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.

R package of dynamic SEIR model.  下载地址： https://github.com/ruiqwy/dSEIR

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.

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