Table 1 List of IDeAl Statistical Software

1. Araujo, A. (2016): R-Code “Statistical Analysis of Series of N-of-1 Trials Using R”, http://www.ideal.rwth-aachen.de/wp-content/uploads/2014/02/nof1_rand_cycles_v8.pdf

2. Brzyski, D. Peterson, C., Candes, E.J., Bogdan, M., Sabatti, C., Sobczyk, P. (2016): R package “geneSLOPE” for genome-wide association studies with SLOPE. https://cran.r-project.org/web/packages/geneSLOPE/index.html

3. Graf, A., Bauer, P., Glimm, E., König, F. (2014): R-Code to calculate worst case type I error inflation in multiarmed clinical trials, http://onlinelibrary.wiley.com/doi/10.1002/bimj.201300153/suppinfo

4. Jobjörnsson, S. (2015): R package “bdpopt” for optimization of Bayesian Decision Problems. https://cran.r-project.org/web/packages/bdpopt/index.html

5. Hlavin, G. (2016): application for extrapolation to adjust significance level based on prior information, http://www.ideal-apps.rwth-aachen.de:3838/Extrapolation/

6. Möllenhoff,K. (2015): R package “TestingSimilarity” for testing similarity of dose response curves. https://cran.r-project.org/web/packages/TestingSimilarity/

7. Riviere, M.K., Mentré, F. (2015): R package “MIXFIM” for the evaluation and optimization of the Fisher Information Matrix in Non-Linear Mixed Effect Models using Markov Chains Monte Carlo for both discrete and continuous data. https://cran.r-project.org/web/packages/MIXFIM/

8. Schindler, D., Uschner, D., Manolov, M, Pham, M., Hilgers, R.-D., Heussen, N. (2016): R package “randomizR” on Randomization for clinical trials. https://cran.r-project.org/web/packages/randomizeR/

9. Senn, S, (2014): R, GenStat and SAS Code for Sample Size Considerations in N-of-1 trials, http://www.ideal.rwth-aachen.de/wp-content/uploads/2014/02/Sample-Size-Considerations-for-N-of-1-trials.zip

10. Sobczyk, P., Josse, J., Bogdan, M. (2015): R package “varclust” for dimensionality reduction via variables clustering. https://psobczyk.shinyapps.io/varclust_online/

11. Sobczyk, P., Josse, J., Bogdan, M. (2017): R package “pesel” Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL). https://github.com/psobczyk/pesel

12. Szulc, P., Frommlet, F., Tang, H., Bogdan, M. (2017): R application for joint genotype and admixture mapping in admixed populations, http://www.math.uni.wroc.pl/~mbogdan/admixtures/

13. Van der Elst, W., Alonso, A., Molenberghs, G. (2017): R package “EffectTreat” on the Prediction of Therapeutic Success. https://cran.r-project.org/web/packages/EffectTreat/index.html

14. Van der Elst, W., Meyvisch, P., Alonso, A., Ensor, H.M., Weir, C.J., Molenberghs, G. (2017): R Package “Surrogate” for evaluation of surrogate endpoints in clinical trials. https://cran.r-project.org/web/packages/Surrogate/

15. Van der Elst, W., Molenberghs, G., Hilgers, R.-D., Heussen, N. (2016): R package “CorrMixed” for the estimation of within subject correlations based on linear mixed effects models. https://cran.r-project.org/web/packages/CorrMixed/index.html