Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step (hierarchical and iterative) cluster analysis as described in Bergman & El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>. While person-centered analysis is increasingly common in psychology, education, and related fields, carrying it out is challenging.

Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.

c(
person(c("Joshua", "M"), "Rosenberg", role = c("aut", "cre"), email = "jmichaelrosenberg@gmail.com"),
person(c("Jennifer", "A"), "Schmidt", role = "aut", email = "jaschmid@msu.edu"),
person(c("Patrick", "N"), "Beymer", role = "aut", email = "beymerpa@msu.edu"),
person(c("Rebecca", "R"), "Steingut", role = "ctb", email = "rebeccarosesteingut@gmail.com")
)
dplyr, tidyr, ggplot2, tibble, irr, lpSolve, purrr, class
Joshua M Rosenberg [aut, cre],
Jennifer A Schmidt [aut],
Patrick N Beymer [aut],
Rebecca R Steingut [ctb]

dplyr, tidyr, ggplot2, tibble, irr, lpSolve, purrr, class,
forcats, magrittr

person("Marjolein", "Fokkema", role = c("aut", "cre"), email = "m.fokkema@fsw.leidenuniv.nl")
Derives prediction rule ensembles (PREs). Largely follows the
procedure for deriving PREs as described in Friedman & Popescu (2008;
<DOI:10.1214/07-AOAS148>), with several adjustments and improvements. The
main function pre() derives a prediction rule ensemble. Functions coef() and
importance() can be used to inspect the generated ensemble. Function predict()
generates predicted values. Singeplot() and pairplot() depict dependence of the
output on specified predictor variables. Function cvpre() performs full cross
validation of a pre to calculate the expected prediction error. Functions
interact() and bsnullinteract() can be used to assess interaction effects of
predictor variables.
R (>= 3.3.0), partykit, glmnet, MatrixModels, Formula,
methods, graphics