person("Gordon", "J. Ross", email="gordon@gordonjross.co.uk", role=c("aut")),
person("Dean", "Markwick", email="dean.markwick@talk21.com", role=c("aut", "cre"))
Description: Perform nonparametric Bayesian analysis using Dirichlet
processes without the need to program the inference algorithms.
Utilise included pre-built models or specify custom
models and allow the dirichletprocess package to handle the
Our Dirichlet process objects can act as building blocks for a variety
of statistical models including and not limited to: density estimation,
clustering and prior distributions in hierarchical models.
Packaged: 2018-04-18 13:56:36 UTC; deanmarkwick

models and allow the 'dirichletprocess' package to handle the
Packaged: 2018-11-23 17:38:05 UTC; deanmarkwick

Packaged: 2019-05-03 15:51:47 UTC; deanmarkwick

Packaged: 2019-12-11 06:57:13 UTC; deanmarkwick

person("Giovanni", "Sighinolfi", email ="giovanni.sighinolfi2@studio.unibo.it", role=c("ctb"))
Packaged: 2020-06-13 10:22:08 UTC; deanmarkwick

Title: Directional Metropolis Hastings Algorithm
Description: Implementation of Directional Metropolis Hastings Algorithm for

Title: Estimation in Dirichlet-Multinomial distribution.
Description: Estimate parameters in Dirichlet-Multinomial and compute
Packaged: Mon Jun 22 10:18:59 2009; tvede

Packaged: Thu Sep 24 09:26:11 2009; tvede

Title: Estimation in Dirichlet-Multinomial Distribution
Description: Estimate parameters in Dirichlet-Multinomial and compute log-likelihoods.

Title: Distributional Regression for Time to Event Data
Authors@R: c(person("Alexander", "Seipp", role = c("aut", "cre"), email = "alexander.seipp@uni-oldenburg.de"),
person("Fabian", "Otto-Sobotka", role = "aut", email = "fabian.otto-sobotka@uni-oldenburg.de"))
Imports: mgcv, splines, formula.tools, nloptr, survival, Matrix, MASS,
Description: Semiparametric distributional regression methods (expectile, quantile and mode regression) for time-to-event variables with right-censoring; uses inverse probability of censoring weights or accelerated failure time models with auxiliary likelihoods. Expectile regression using inverse probability of censoring weights has been introduced in Seipp et al. (2021) "Weighted Expectile Regression for Right-Censored Data" <doi:10.1002/sim.9137>, mode regression for time-to-event variables has been introduced in Seipp et al. (2022) "Flexible Semiparametric Mode Regression for Time-to-Event Data" <doi:10.1177/09622802221122406>.