Monotone multi-layer perceptron neural network
Multi-layer perceptron neural network with partial
monotonicity constraints

Monotone Multi-Layer Perceptron Neural Network
Train and make predictions from a multi-layer perceptron neural
network with partial monotonicity constraints.

Train and make predictions from a multi-layer perceptron neural
network with optional partial monotonicity constraints.

Multi-Layer Perceptron Neural Network with Optional Monotonicity
Constraints

Perform Monothetic Clustering with Extensions to Circular
c(person(given = "Tan",
family = "Tran",
role = c("aut", "cre"),
email = "vinhtantran@gmail.com",
comment = c(ORCID = "0000-0001-9881-6339")),
person(given = "Brian",
family = "McGuire",
role = "aut",
email = "mcguirebc@gmail.com"),
person(given = "Mark",
family = "Greenwood",
role = "aut",
email = "greenwood@montana.edu",
comment = c(ORCID = "0000-0001-6933-1201")))
Implementation of the Monothetic Clustering
algorithm (Chavent, 1998 <doi:10.1016/S0167-8655(98)00087-7>) on
continuous data sets. A lot of extensions are included in the package,
including applying Monothetic clustering on data sets with circular
variables, visualizations with the results, and permutation and
cross-validation based tests to support the decision on the number of
clusters.
cluster, doParallel, dplyr (>= 1.0.0), foreach, ggplot2,
graphics, grDevices, parallel, permute, purrr, rlang (>=
0.3.0), stats, stringr, tibble, tidyr
Tan Tran [aut, cre] (<https://orcid.org/0000-0001-9881-6339>),
Brian McGuire [aut],
Mark Greenwood [aut] (<https://orcid.org/0000-0001-6933-1201>)

Perform Monothetic Clustering with Extensions to Circular Data
cli (>= 2.0.0), cluster, doParallel, dplyr (>= 1.0.0),
foreach, ggplot2, graphics, grDevices, parallel, permute,
purrr, rlang (>= 0.3.0), stats, stringr, tibble (>= 3.0.0),
tidyr

cluster (>= 2.0.5), doParallel, dplyr (>= 1.0.0), foreach,
ggplot2, graphics, grDevices, parallel, permute, purrr (>=
0.3.0), rlang (>= 0.3.0), stats, stringr (>= 0.5), tibble (>=
3.0.0), tidyr (>= 1.0.0)