Bayesian Constrained Generalised Linear Models
c(
person("Cristian", "Oliva-Aviles", email = "coliva@colostate.edu", role = c("aut", "cre")),
person("Mary C.", "Meyer", email="meyer@stat.colostate.edu", role="aut"))
Fits generalised partial linear regression models using a Bayesian approach, where shape and
smoothness constraints are imposed on nonparametrically modelled predictors through shape-restricted splines, and
no constraints are imposed on optional parametrically modelled covariates. See Meyer et al. (2011) <doi/10.1080/10485252.2011.597852>
for more details. IMPORTANT: before installing 'bcgam', you need to install 'Rtools' (Windows) or 'Xcode' (Mac OS X). These are
required for the correct installation of 'nimble' (<https://r-nimble.org/download>).
Rtools (>= 3.3) for Windows, Xcode (>= 9.0) for Mac
OS X
Cristian Oliva-Aviles [aut, cre],
Mary C. Meyer [aut]

Bayesian clustering using spike-and-slab hierarchical model,
suitable for clustering high-dimensional data.
Vahid PARTOVI NIA and Anthony C. DAVISON
The package builds a dendrogram with log posterior as a
natural distance defined by the model. It is also capable to
computing Bayesian discrimination probabilities equivalent to
the implemented Bayesian clustering. Spike-and-Slab models are
adopted in a way to be able to produce an importance measure
for clustering and discriminant variables. The method works
properly for data with small sample size and high dimensions.
The model parameter estimation maybe difficult, depending on
data structure and the chosen distribution family.

Bayesian Hierarchical Clustering Using Spike and Slab Models
R (>= 3.2.1), stats, grDevices, graphics
Builds a dendrogram using log posterior as a natural distance defined by the model and meanwhile waits the clustering variables. It is also capable to computing equivalent Bayesian discrimination probabilities. The adopted method suites small sample large dimension setting. The model parameter estimation maybe difficult, depending on data structure and the chosen distribution family.