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.

Perform correlation and linear regression test
    among the numeric fields in a data.frame automatically
    and make plots using pairs or lattice::parallelplot.

Plot the daily and cumulative number of downloads of your packages.
    It is designed to be slightly more convenient than the several similar programs. If you want to run this each morning,
    you do not need to keep typing in the names of your packages. Also, this combines the daily and cumulative counts in one
    run, you do not need to run separate programs to get both types of information.

Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. (2017) <doi:10.1093/bioinformatics/btx835>). The aim of this tool is to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest (Drost et al. (2016) <doi:10.1101/051565>).

We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.

Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS.  The n1qn1 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 
       <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.