Imports: bit64, DBI (>= 0.8), methods, odbc (>= 1.1.6), rstudioapi (>=

person("Koji", "Makiyama", email = "hoxo.smile@gmail.com", role = c("cre", "aut")),

Title: RStudio Addin for Teaching and Learning Data Manipulation Using
shinyWidgets, shinyAce, tidyverse, tidyr, stringr, magrittr,
Authors@R: person("Keon-Woong", "Moon", email = "cardiomoon@gmail.com",
Description: An RStudio addin for teaching and learning data manipulation using the 'dplyr' package.
You can learn each steps of data manipulation by clicking your mouse without coding.
You can get resultant data (as a 'tibble') and the code for data manipulation.
Packaged: 2017-09-04 15:26:43 UTC; cardiomoon

Package: dpmixsim
Version: 0.0-3
Date: 2010-04-29
Title: Dirichlet Process Mixture model simulation for clustering and
image segmentation
Author: Adelino Ferreira da Silva <afs@fct.unl.pt>
Maintainer: Adelino Ferreira da Silva <afs@fct.unl.pt>
Depends: R (>= 2.9.2), oro.nifti
Suggests: cluster
Description: The package implements a Dirichlet Process mixture (DPM)
model for clustering and image segmentation. The DPM model is a
Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components. The code implements conjugate models with normal
structure (conjugate normal-normal DP mixture model). The
package's applications are oriented towards the classification
of magnetic resonance images according to tissue type or region
of interest.
License: GPL (>= 2)
Repository: CRAN
Packaged: 2010-04-29 08:18:06 UTC; afs
Date/Publication: 2010-04-29 09:20:45

Package: dpmixsim
Version: 0.0-5
Date: 2010-09-06
Title: Dirichlet Process Mixture model simulation for clustering and
image segmentation
Author: Adelino Ferreira da Silva <afs@fct.unl.pt>
Maintainer: Adelino Ferreira da Silva <afs@fct.unl.pt>
Depends: R (>= 2.10.0), oro.nifti, cluster
Description: The package implements a Dirichlet Process Mixture (DPM)
model for clustering and image segmentation. The DPM model is a
Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components. The code implements conjugate models with normal
structure (conjugate normal-normal DP mixture model). The
package's applications are oriented towards the classification
of magnetic resonance images according to tissue type or region
of interest.
License: GPL (>= 2)
Repository: CRAN
Packaged: 2010-09-07 07:32:43 UTC; afs
Date/Publication: 2010-09-07 08:50:58