tpopt for the construction of $T_P$-optimal designs, the routine KLopt.lnorm for the calculation of $KL$-optimal designs (for lognormal errors) and several auxiliary procedures to represent the results. Function tpopt is based on the algorithms that were developed in [7]. Function KLopt.lnorm is based on the methodology proposed in [8]. See the references for more details. It is planned to add several new routines for different types of discriminative designs.
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[3] Dette H., Pepelyshev A. (2008) Efficient experimental designs for sigmoidal growth models. Journal of statistical planning and inference, vol. 138, pp. 2--17.
[4] Dette H., Melas V.B., Shpilev P. (2013) Robust T-optimal discriminating designs. Annals of Statistics, vol. 41(4), pp. 1693--1715.
[5] Braess D., Dette H. (2013) Optimal discriminating designs for several competing regression models. Annals of Statistics, vol. 41(2), pp. 897--922.
[6] Braess D., Dette H. (2013) Supplement to ``Optimal discriminating designs for several competing regression models''. Annals of Statistics, online supplementary material. [7] Dette H., Melas V.B., Guchenko R. (2014) Bayesian T-optimal discriminating designs. ArXiv link. [8] Dette H., Guchenko R., Melas V.B. (2015) Efficient computation of Bayesian optimal discriminating designs. ArXiv link.