Searching for Optimal MDS Procedure for Metric and
Interval-Valued Data

R (>= 3.6.0), smacof, clusterSim, symbolicDA

c(person(given = "Marek",
family = "Walesiak",
role = c("aut"),
email = "marek.walesiak@ue.wroc.pl",
comment=c(ORCID = "0000-0003-0922-2323")),
person(given = "Andrzej",
family = "Dudek",
role = c("aut","cre"),
email = "andrzej.dudek@ue.wroc.pl",
comment = c(ORCID = "0000-0002-4943-8703")))

Selecting the optimal multidimensional scaling (MDS) procedure for metric data via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal). Selecting the optimal multidimensional scaling (MDS) procedure for interval-valued data via metric MDS (ratio, interval, mspline).Selecting the optimal multidimensional scaling procedure for interval-valued data by varying all combinations of normalization and optimization methods.Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties.
(Borg, I., Groenen, P.J.F., Mair, P. (2013) <doi:10.1007/978-3-642-31848-1>,
Walesiak, M. (2016) <doi:10.15611/ekt.2016.2.01>,
Walesiak, M. (2017) <doi:10.15611/ekt.2017.3.01>).

Improved Score Tests for Generalized Linear Models
c(person("Antonio Hermes M.","da Silva-Junior",
email = "hermes@ccet.ufrn.br",
role = c("aut", "cre")),
person("Damiao N.","da Silva",
email = "damiao@ccet.ufrn.br",
role = "aut"),
person("Silvia L. P.", "Ferrari",
email = "silviaferrari.usp@gmail.com",
role = "ctb"))
Antonio Hermes M. da Silva-Junior [aut, cre], Damiao N. da Silva [aut], Silvia L. P. Ferrari [ctb]
Antonio Hermes M. da Silva-Junior <hermes@ccet.ufrn.br>
A set of functions to obtain modified score test for generalized linear models.

Functions for Analysis of Data with General Recognition Theory
Robert X.D. Hawkins <rxdh@stanford.edu>, Joe Houpt
<joseph.houpt@wright.edu>, Noah Silbert <noahpoah@gmail.com>, Leslie
Blaha <Leslie.Blaha@wpafb.af.mil>, Thomas D. Wickens
<twickens@socrates.berkeley.edu>
This package contains a series of tools associated with General
Recognition Theory (Townsend & Ashby, 1986), including Gaussian model fitting of 4x4 and more
general confusion matrices, associated plotting and model comparison tools,
and tests of marginal response invariance and report independence.