Translate R to Python for a tool of high throughput data analysis

I need the following project translated from R to Python. Surrogate Variable Analysis (SVA) [login to view URL] This tool is very useful for biomedical research. The file ComBat.R has already been translated by someone at [login to view URL] It is likely that not all files need to be translated. The only things that are needed are the codes that leads to the line fit = lmFit(edata,modSv) in the documentation. i.e., only the calculation of modSV is needed. The translated version will be open access on GibHub for researchers to use and will be integrated into the rosely library [login to view URL] The SVA tools are used for data pre-processing (to remove all the unknown confounders) and then used in rosely (similar to limma in the R documentation of SVA). Ideally, the translated version should include a function that takes the data in and output the data with all the confounders removed. The requirement is that it should be tested and produce the exact same results with the R version for examples shown in the R documentation pdf files (use a python notebook to show this). It should also contain a clear documentation of the usage. The price is negotiable. But keep in mind that this comes from public funding and I will have to negotiable with the institute (which plays by rules) for this to happen. However, if you always want to contribute to science and help to cure cancer and other diseases, this will be one of the chances.