This package provides several functions suitable for efficient numerical construction of optimal discriminative designs.

At the current state this package provides the routine `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.

[1] Atkinson A.C., Fedorov V.V. (1975) *The design of experiments for discriminating between two rival models*. Biometrika, vol. 62(1), pp. 57–70.

[2] Atkinson A.C., Fedorov V.V. (1975) *Optimal design: Experiments for discriminating between several models*. Biometrika, vol. 62(2), pp. 289–303.

[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.

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