Title: Adaptive Bioequivalence Design for In-Vitro Permeation Tests
person("Daeyoung", "Lim", email = "daeyoung.lim@uconn.edu",
person("Elena", "Rantou", email = "Elena.Rantou@fda.hhs.gov", role = "ctb"),
person("Jessica", "Kim", email = "Jessica.Kim@fda.hhs.gov", role = "ctb"),
person("Sungwoo", "Choi", email = "Sungwoo.Choi@fda.hhs.gov", role = "ctb"),
person("Nam Hee", "Choi", email = "NamHee.Choi@fda.hhs.gov", role = "ctb"),
person("Stella", "Grosser", email = "Stella.Grosser@fda.hhs.gov", role = "ctb"))
Description: Contains functions carrying out adaptive procedures using mixed scaling approach to establish bioequivalence for in-vitro permeation test (IVPT) data. Currently, the package provides procedures based on parallel replicate design and balanced data, according to the U.S. Food and Drug Administration's "Draft Guidance on Acyclovir" <https:www.accessdata.fda.gov/drugsatfda_docs/psg/Acyclovir_topical cream_RLD 21478_RV12-16.pdf>. Potvin et al. (2008) <doi:10.1002/pst.294> provides the basis for our adaptive design (see Method B). This package reflects the views of the authors and should not be construed to represent the views or policies of the U.S. Food and Drug Administration.

Title: Implementation of a generic adaptive Monte Carlo Markov Chain
Author: Andreas Scheidegger, <andreas.scheidegger@eawag.ch>, <scheidegger.a@gmail.com>
Description: This package provides an implementation of the generic adaptive Monte
Carlo Markov chain sampler proposed by Vihola (2011).
Depends: R (>= 2.14.1), parallel, coda, Matrix

Description: This package provides an implementation of the generic
adaptive Monte Carlo Markov chain sampler proposed by Vihola

Title: Implementation of a Generic Adaptive Monte Carlo Markov Chain
Description: Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.