Provides native R access to Interactive Brokers Trader Workstation API.

Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). <https://pubmed.ncbi.nlm.nih.gov/12069320/>. Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).<doi:10.1007/s11356-012-1359-1>. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). <doi:10.1007/s11356-013-2169-9>. Minato N. (2022). <https://minato.sip21c.org/msb/>.

Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function.

Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible)
             using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is 
             evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance
             are computed for variable selection. The thresholds values for variable selection are 
             computed using a nonparametric permutation test. 
             See 'Cyprien Mbogning' and 'Philippe Broet' (2016)<doi:10.1186/s12859-016-1090-x> for 
             an overview about the methods implemented in this package.

Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.

Implementation of the classifier described in the paper Ali HR et al (2014) <doi:10.1186/s13059-014-0431-1>. It uses copy number and/or expression form breast cancer data, trains a Tibshirani's 'pamr' classifier with the features available and predicts the iC10 group.