Title: Integrative immunoinformatics for Mycobacterial diseases in R
Author: Deepika Kulshreshtha, Rupanjali Chaudhuri, S. Ramachandran
Description: The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.

Title: Integrative Immunoinformatics for Mycobacterial Diseases in R
Author: Deepika Kulshreshtha, Rupanjali Chaudhuri, Surabhi Seth, S. Ramachandran
Packaged: 2022-05-13 09:32:12 UTC; sramachandran

Title: Automatic Correlation and Regression Test in a 'data.frame'
Description: Perform correlation and linear regression test
among the numeric fields in a data.frame automatically
and make plots using pairs or lattice::parallelplot.
Packaged: 2018-04-10 12:50:03 UTC; cardiomoon

Title: Automatic Correlation and Regression Test in a Data Frame
among the numeric columns in a data frame automatically
Packaged: 2014-10-01 23:07:17 UTC; cardiomoon

Description: MyEpisodes stats and useful functions Currently just for
show info from a list, see \link{shows_from_myepisodes}

Description: Useful functions for accessing MyEpisodes feeds and
episode information as well as other tv episode related actions

Title: Regression Models for Monozygotic Twin Data
Description: Linear and logistic regression models for quantitative genetic analysis of data from monozygotic twins.
Packaged: 2015-01-14 18:20:24 UTC; Juan StepSon

Title: Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGS
Authors@R: c(person("Matthew", "Fidler", role=c("aut","cre"), email= "matthew.fidler@gmail.com"),
person("Wenping", "Wang", role = "aut", email = "wwang8198@gmail.com"),
Description: Provides 'Scilab' 'n1qn1', or Quasi-Newton BFGS
"qn" without constraints.  This takes more memory than traditional L-BFGS.  This
routine is useful since it allows prespecification of a Hessian.  If the Hessian is near enough the
truth in optimization it can speed up the optimization problem. The algorithm is described in the
'Scilab' optimization documentation located at