Title: Tools for Nonparametric Martingale Posterior Sampling
person("Blake", "Moya", email = "blakemoya@utexas.edu",
person("The University of Texas at Austin", role = c("cph", "fnd")))
Description: Performs Bayesian nonparametric density estimation using Martingale
posterior distributions and including the Copula Resampling (CopRe) algorithm.
Also included are a Gibbs sampler for the marginal Mixture of Dirichlet Process
(MDP) model and an extension to include full uncertainty quantification via a
new Polya completion algorithm for the MDP. The CopRe and Polya samplers
generate random nonparametric distributions as output, leading to complete
nonparametric inference on posterior summaries. Routines for calculating
arbitrary functionals from the sampled distributions are included as well as an
important algorithm for finding the number and location of modes, which can
then be used to estimate the clusters in the data using, for example, k-means.
Implements work developed in Moya B., Walker S. G. (2022)
Imports: Rcpp, pracma, ggplot2, abind, dirichletprocess
The University of Texas at Austin [cph, fnd]

Title: Sample Size Calculation for Two Primary Time-to-Event Endpoints
Description: Computes the required number of patients for two time-to-event end-points as primary endpoint in phase III clinical trial.
Depends: stats, gsDesign, digest, plyr, proto
Collate: 'datacheck.R' 'nbevent.R' 'probanofix.R' 'probafix.R'

Title: Cluster Optimized Proximity Scaling
Authors@R: c(person(given="Thomas", family="Rusch", email="thomas.rusch@wu.ac.at", role = c("aut","cre"), comment = c(ORCID = "0000-0002-7773-2096")), person(given="Jan",family="de Leeuw", role = c("aut")), person(given="Patrick",family="Mair", role = "aut"))
Description: Cluster optimized proximity scaling (COPS) refers to multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration. They achieve this by transforming proximities/distances with power functions and augment the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly for given parameters (COPS-C) for ratio, interval and non-metric MDS (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal parameters (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a COPS framework like ratio, interval and non-metric MDS for COPS-C and P-COPS with Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459), scaling by majorizing a complex function (SMACOF; de Leeuw, 1977, <https://escholarship.org/uc/item/4ps3b5mj>), Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>), elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>), s-stress (Takane, Young & de Leeuw, 1977, <doi:10.1007/BF02293745>, r-stress (de Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>), power-stress (Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>) and power elastic scaling, power Sammon mapping and approximated power stress (Rusch, Mair & Hornik, 2015, <https://bach-s59.wu.ac.at/4888/>). All of these models can also solely be fit as MDS with power transformations. The package further contains a function for pattern search optimization, the "Adaptive Luus-Jakola Algorithm" (Rusch, Mair & Hornik, 2015, <https://bach-s59.wu.ac.at/4888/>).
Imports: MASS, minqa, pso, scatterplot3d, NlcOptim, Rsolnp, dfoptim,
subplex, cmaes, crs, nloptr, rgl, rgenoud, GenSA
Author: Thomas Rusch [aut, cre] (<https://orcid.org/0000-0002-7773-2096>),