survival. The procedure was proposed in Wang et al. (2016) <doi:10.1111/biom.12594>.
It considers missing data imputation with a sensitivity
Imports: sqldf (>= 0.4), survival (>= 2.38), parallel (>= 3.2), methods
Suggests: knitr, shiny, rmarkdown, pander, DT, shinythemes

survival. The procedure was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594>.

survival. The procedure was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594>
and Wang et al. (2020) <DOI:10.18637/jss.v093.i12>. It considers missing data
analysis strategies to handle the unobserved functional outcomes not due to

Imports: rstan (>= 2.18.1), sqldf (>= 0.4), survival (>= 2.38), mice

Imports: tcltk, tkrplot, grDevices, graphics, stats
Suggests: rggobi, DendSer, cluster, RColorBrewer, datasets
Description: 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.

'GGobi' interactive plots and user-supplied plots.

(<https://github.com/tsieger/idendro>) to base R graphics and

Description: A fast way to clean and obtain unique identifiers used by the Los Angeles County Probation Department.
Imports: stringr (>= 1.0.0), magrittr (>= 1.5)

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

Title: Jacquard Condensed Coefficients of Identity
Description: Calculate identity coefficients, based on Mark Abney's C code.
Packaged: Thu Mar 29 12:15:42 2007; nali

Author: Na (Michael) Li <wuolong@gmail.com>
Maintainer: Na (Michael) Li <wuolong@gmail.com>
Description: Calculate identity coefficients, based on Mark Abney's C

Packaged: Tue Feb  5 15:27:41 2008; nali

Title: Bayesian Dynamic Spatio-Temporal Models, Including the
c(person("Easton", "Huch", email = "easton.huch@gmail.com", role = c("aut", "cre")),
person("Robert", "Richardson", email = "richardson@stat.byu.edu", rol = ("ths")))
Description: In contrast to other methods of modeling spatio-temporal data,
dynamic spatio-temporal models (DSTMs) directly model the dynamic
'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.
Noel Cressie and Christopher K. Wikle (2011, ISBN:978-0471692744).
Christopher K. Wikle and Noel Cressie (1999, <https://www.jstor.org/stable/2673587>)
Imports: Rcpp, matrixcalc, pdist, mvtnorm