===========================================================
                                      .___ __  __   
          _________________  __ __  __| _/|__|/  |_ 
         / ___\_` __ \__  \ |  |  \/ __ | | \\_  __\
        / /_/  >  | \// __ \|  |  / /_/ | |  ||  |  
        \___  /|__|  (____  /____/\____ | |__||__|  
       /_____/            \/           \/           
              grep rough audit - static analysis tool
                  v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_CLL.Rmd-91-
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_CLL.Rmd:92:* **numFactors**: number of factors (default is 0.5 times the number of samples). By default, the model will only remove a factor if it explains exactly zero variance in the data. You can increase this threshold on minimum variance explained by setting `TrainOptions$dropFactorThreshold` to a value higher than zero.  
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_CLL.Rmd-93-
##############################################
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_CLL.Rmd-105-
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_CLL.Rmd:106:* **maxiter**: maximum number of iterations. Ideally set it large enough and use the convergence criterion `TrainOptions$tolerance`.  
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_CLL.Rmd-107-
##############################################
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_scMT.Rmd-65-
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_scMT.Rmd:66:* **numFactors**: number of factors (default is 0.5 times the number of samples). By default, the model will only remove a factor if it explains exactly zero variance in the data. You can increase this threshold on minimum variance explained by setting `TrainOptions$dropFactorThreshold` to a value higher than zero.  
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_scMT.Rmd-67-
##############################################
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_scMT.Rmd-79-
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_scMT.Rmd:80:* **maxiter**: maximum number of iterations. Ideally set it large enough and use the convergence criteria `TrainOptions$tolerance`.  
r-bioc-mofa-1.6.1+dfsg/inst/doc/MOFA_example_scMT.Rmd-81-
##############################################
r-bioc-mofa-1.6.1+dfsg/mofapy/run/python_template.py-19-# factors: number of factors. By default, the model does not automatically learn the number of factors. 
r-bioc-mofa-1.6.1+dfsg/mofapy/run/python_template.py:20:# 	If you want the model to do this (based on a minimum variance explained criteria), set `TrainOptions$dropFactorThreshold` to a non-zero value.
r-bioc-mofa-1.6.1+dfsg/mofapy/run/python_template.py-21-# likelihoods: list with the likelihood for each view. Usually we recommend: 
##############################################
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_CLL.Rmd-91-
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_CLL.Rmd:92:* **numFactors**: number of factors (default is 0.5 times the number of samples). By default, the model will only remove a factor if it explains exactly zero variance in the data. You can increase this threshold on minimum variance explained by setting `TrainOptions$dropFactorThreshold` to a value higher than zero.  
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_CLL.Rmd-93-
##############################################
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_CLL.Rmd-105-
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_CLL.Rmd:106:* **maxiter**: maximum number of iterations. Ideally set it large enough and use the convergence criterion `TrainOptions$tolerance`.  
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_CLL.Rmd-107-
##############################################
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_scMT.Rmd-65-
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_scMT.Rmd:66:* **numFactors**: number of factors (default is 0.5 times the number of samples). By default, the model will only remove a factor if it explains exactly zero variance in the data. You can increase this threshold on minimum variance explained by setting `TrainOptions$dropFactorThreshold` to a value higher than zero.  
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_scMT.Rmd-67-
##############################################
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_scMT.Rmd-79-
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_scMT.Rmd:80:* **maxiter**: maximum number of iterations. Ideally set it large enough and use the convergence criteria `TrainOptions$tolerance`.  
r-bioc-mofa-1.6.1+dfsg/vignettes/MOFA_example_scMT.Rmd-81-