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optad
adoptr:Adaptive Optimal Two-Stage Designs
Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.
Maintained by Maximilian Pilz. Last updated 6 months ago.
1 stars 7.09 score 39 scripts 1 dependents
faosorios
MVT:Estimation and Testing for the Multivariate t-Distribution
Routines to perform estimation and inference under the multivariate t-distribution <doi:10.1007/s10182-022-00468-2>. Currently, the following methodologies are implemented: multivariate mean and covariance estimation, hypothesis testing about equicorrelation and homogeneity of variances, the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate generation.
Maintained by Felipe Osorio. Last updated 1 hours ago.
3.53 score 34 scripts
stla
boodist:Some Distributions from the 'Boost' Library and More
Make some distributions from the 'C++' library 'Boost' available in 'R'. In addition, the normal-inverse Gaussian distribution and the generalized inverse Gaussian distribution are provided. The distributions are represented by 'R6' classes. The method to sample from the generalized inverse Gaussian distribution is the one given in "Random variate generation for the generalized inverse Gaussian distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.
Maintained by Stéphane Laurent. Last updated 1 years ago.
3.04 score 22 scripts