Functions to Perform Hierarchical Analysis of Distance Sampling
Data
Functions for performing hierarchical analysis of distance
sampling data, with ability to use an areal spatial ICAR model on
top of user supplied covariates to get at variation in abundance
intensity.  The detection model can be specified as a function of
observer and individual covariates, where a parametric model is
supposed for the population level distribution of covariate values.
The model uses data augmentation and a reversible jump MCMC
algorithm to sample animals that were never observed.  Also
included is the ability to include point independence (increasing
correlation multiple observer's observations as a function of
distance, with independence assumed for distance=0 or first
distance bin), as well as the ability to model species
misclassification rates using a multinomial logit formulation on data
from double observers.  There is also the the ability to
include zero inflation, but this is only recommended for cases where
sample sizes and spatial coverage of the survey are high.
truncnorm, mvtnorm, Matrix, coda, xtable, mc2d, ggplot2,
rgeos, MCMCpack

Set Data Visualization Using Hierarchies
Pure set data visualization approaches are often limited in
scalability due to the combinatorial explosion of distinct set families as
the number of sets under investigation increases. hierarchicalSets applies
a set centric hierarchical clustering of the sets under investigation and
uses this hierarchy as a basis for a range of scalable visual
representations. hierarchicalSets is especially well suited for collections
of sets that describe comparable comparable entities as it relies on the
sets to have a meaningful relational structure.
ggdendro, ggplot2 (>= 2.0.0), stats, Rcpp, scales, Matrix,
MASS, RColorBrewer, gtable, grDevices, methods, utils
'RcppExports.R' 'hSet.R' 'hierarchicalSet.R'
'hierarchicalSets-package.R' 'twitter.R' 'visuals.R'

Implementation of the convex banding procedure (using a
hierarchical group lasso penalty) for covariance estimation that is
introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance
Matrix. Accepted for publication in JASA.
Jacob Bien <jbien@cornell.edu>, Florentina Bunea, and Luo Xiao