hitandrun

R package hitandrun: ”Hit and Run” and ”Shake and Bake” for Sampling Uniformly from Convex Shapes. The ”Hit and Run” Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the ”Shake and Bake” method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.

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References in zbMATH (referenced in 4 articles )

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  1. Bhattacharya, Rabi; Oliver, Rachel: Superiority of Bayes estimators over the MLE in high dimensional multinomial models and its implication for nonparametric Bayes theory (2020)
  2. Xiong, Zikang; Liu, Liwei; Ning, Jianhui; Qin, Hong: Sphere packing design for experiments with mixtures (2020)
  3. Cao, Yongtao; Smucker, Byran J.; Robinson, Timothy J.: A hybrid elitist Pareto-based coordinate exchange algorithm for constructing multi-criteria optimal experimental designs (2017)
  4. Director, Hannah M.; Gattiker, James; Lawrence, Earl; van der Wiel, Scott: Efficient sampling on the simplex with a self-adjusting logit transform proposal (2017)