tcltk, tkrplot, grDevices, graphics, stats
rggobi, DendSer, cluster, RColorBrewer, datasets
Interactive dendrogram that enables the user to select and
color clusters, to zoom and pan the dendrogram, and to visualize
the clustered data not only in a built-in heat map, but also in
GGobi interactive plots and user-supplied plots.
This is a backport of Qt-based 'idendro'
(https://github.com/tsieger/idendro) to base R graphics and Tcl/Tk GUI.

Interactive dendrogram that enables the user to select and
color clusters, to zoom and pan the dendrogram, and to visualize
the clustered data not only in a built-in heat map, but also in
'GGobi' interactive plots and user-supplied plots.
This is a backport of Qt-based 'idendro'
(https://github.com/tsieger/idendro) to base R graphics and Tcl/Tk GUI.

Interactive dendrogram that enables the user to select and
color clusters, to zoom and pan the dendrogram, and to visualize
the clustered data not only in a built-in heat map, but also in
'GGobi' interactive plots and user-supplied plots.
This is a backport of Qt-based 'idendro'
(<https://github.com/tsieger/idendro>) to base R graphics and
Tcl/Tk GUI.

A fast way to clean and obtain unique identifiers used by the Los Angeles County Probation Department.

A fast way to clean unique identifiers used by the Los Angeles County Probation Department.

Jacquard Condensed Coefficients of Identity
Calculate identity coefficients, based on Mark Abney's C code.

Calculate identity coefficients, based on Mark Abney's C
code.

Bayesian Dynamic Spatio-Temporal Models, Including the
Integrodifference Equation Model
c(person("Easton", "Huch", email = "easton.huch@gmail.com", role = c("aut", "cre")),
person("Robert", "Richardson", email = "richardson@stat.byu.edu", rol = ("ths")))
In contrast to other methods of modeling spatio-temporal data,
dynamic spatio-temporal models (DSTMs) directly model the dynamic
data-generating process.
'ideq' supports two main classes of DSTMs:
(1) empirical orthogonal function (EOF) models and
(2) integrodifference equation (IDE) models.
EOF models do not directly use any spatial information;
instead, they make use of observed relationships in the data
(the principal components) to model the underlying process.
In contrast, IDE models are based on diffusion dynamics and the process
evolution is governed by a (typically Gaussian) redistribution kernel.
Both types have a variety of options for specifying the model components,
including the process matrix, process error, and observation error.
The classic reference for DSTMs is
Noel Cressie and Christopher K. Wikle (2011, ISBN:978-0471692744).
For IDE models specifically, see
Christopher K. Wikle and Noel Cressie (1999, <https://www.jstor.org/stable/2673587>)
and
Christopher K. Wikle (2002, <doi:10.1191/1471082x02st036oa>).
Easton Huch [aut, cre],
Robert Richardson [ths]