A toolkit for absolute and relative dating and analysis of
chronological patterns. This package includes functions for
chronological modeling and dating of archaeological assemblages from
count data. It provides methods for matrix seriation. It also allows
to compute time point estimates and density estimates of the occupation
and duration of an archaeological site.
arkhe (>= 1.0.0), dimensio (>= 0.3.0), extraDistr, ggplot2,
grDevices, Hmisc, methods, rlang, stats, utils
covr, folio (>= 1.3.0), khroma, knitr, rmarkdown, tabula (>=
2.0.0), testthat (>= 3.0.0), vdiffr (>= 1.0.0)
'AllClasses.R' 'AllGenerics.R' 'aoristic.R' 'apportion.R'
'coerce.R' 'deprecate.R' 'event_date.R' 'event_plot.R'
'event_resample.R' 'fit.R' 'kairos-package.R' 'mcd.R'
'mutators.R' 'plot.R' 'reexport.R' 'seriate_average.R'
'seriate_permute.R' 'seriate_rank.R' 'seriate_refine.R'
'show.R' 'subset.R' 'utilities.R' 'validate.R' 'zzz.R'

'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations.
Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
data.table (>= 1.14.2), maxLik (>= 1.5-2), ggplot2 (>=
3.3.6), gridExtra (>= 2.3), knitr, rmarkdown, testthat

c(
person("Alexander", "Foss", email = "alexanderhfoss@gmail.com",
role = c("aut", "cre")),
person("Marianthi", "Markatou", role = c("aut"))
)
Alexander Foss [aut, cre],
Marianthi Markatou [aut]
Implements methods for clustering mixed-type data,
specifically combinations of continuous and nominal data. Special attention
is paid to the often-overlooked problem of equitably balancing the
contribution of the continuous and categorical variables. This package
implements KAMILA clustering, a novel method for clustering
mixed-type data in the spirit of k-means clustering. It does not require
dummy coding of variables, and is efficient enough to scale to rather large
data sets. Also implemented is Modha-Spangler clustering, which uses a
brute-force strategy to maximize the cluster separation simultaneously in the
continuous and categorical variables.
stats, abind, KernSmooth, gtools, Rcpp, mclust, plyr