Access Men's Basketball Play by Play Data
c(person(given = "Saiem",
family = "Gilani",
role = c("aut", "cre"),
email = "saiem.gilani@gmail.com"),
person(given = "Jason",
family = "Lee",
role = c("ctb"),
email = "Jason@aisportsfirm.com"),
person(given = "Billy",
family = "Fryer",
role = c("ctb"),
email = "billyfryer@att.net"))
crayon (>= 1.3.4), curl, DBI, ggplot2, ggrepel, qs (>=
0.25.1), rmarkdown, RSQLite, stats, stringi, testthat, tibble
(>= 3.0), xml2 (>= 1.3), yaml
Saiem Gilani [aut, cre],
Jason Lee [ctb],
Billy Fryer [ctb]

Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)
Katherine S. Pollard <kpollard@soe.ucsc.edu>, with
Mark J. van der Laan <laan@stat.berkeley.edu>.
The HOPACH clustering algorithm builds a hierarchical tree
of clusters by recursively partitioning a data set, while ordering
and possibly collapsing clusters at each level. The algorithm uses
the Mean/Median Split Silhouette (MSS) criteria to identify the
level of the tree with maximally homogeneous clusters. It also runs
the tree down to produce a final ordered list of the elements. The
non-parametric bootstrap allows one to estimate the probability that
each element belongs to each cluster (fuzzy clustering).

Katherine S. Pollard <kpollard@wald.ucdavis.edu>, with
Mark J. van der Laan <laan@stat.berkeley.edu>.

Hierarchical Ordered Partitioning and Collapsing Hybrid
(HOPACH)
Katherine S. Pollard, with Mark J. van der Laan <laan@stat.berkeley.edu> and Greg Wall
The HOPACH clustering algorithm builds a hierarchical
tree of clusters by recursively partitioning a data set, while
ordering and possibly collapsing clusters at each level. The
algorithm uses the Mean/Median Split Silhouette (MSS) criteria
to identify the level of the tree with maximally homogeneous
clusters. It also runs the tree down to produce a final
ordered list of the elements. The non-parametric bootstrap
allows one to estimate the probability that each element
belongs to each cluster (fuzzy clustering).

Katherine S. Pollard, with Mark J. van der Laan
<laan@stat.berkeley.edu> and Greg Wall
R (>= 2.11.0), cluster, Biobase, methods
Biobase, cluster, graphics, grDevices, methods, stats, utils
The HOPACH clustering algorithm builds a hierarchical tree
of clusters by recursively partitioning a data set, while
ordering and possibly collapsing clusters at each level. The
algorithm uses the Mean/Median Split Silhouette (MSS) criteria
to identify the level of the tree with maximally homogeneous
clusters. It also runs the tree down to produce a final ordered
list of the elements. The non-parametric bootstrap allows one
to estimate the probability that each element belongs to each
cluster (fuzzy clustering).