person("Grzegorz",
"Smoliński",
role = c("aut", "cre"),
email = "g.smolinski1@gmail.com")
Duplicated data can exist in different rows and columns and user may need to
treat observations (rows) connected by duplicated data as one observation,
e.g. companies can belong to one family (and thus: be one company) by sharing
some telephone numbers. This package provides a function to find connected rows
based on data on chosen columns and collapse it into one row.
rmarkdown, knitr, kableExtra, magrittr, testthat (>= 3.0.0)

Duplicated data can exist in different rows and columns and user may need to
treat observations (rows) connected by duplicated data as one observation,
e.g. companies can belong to one family (and thus: be one company) by sharing
some telephone numbers. This package allows to find connected rows
based on data on chosen columns and collapse it into one row.

Explore neural networks in a layer oriented way, the framework
is intended to give the user total control of the
internals of a net without much effort. Use classes like PerceptronLayer
to create a layer of Percetron neurons, and specify how many you want. The
package does all the tricky stuff internally leaving you focused in what
you want. I wrote this package during a neural networks course to help me
with the problem set.

Clustering of Short Texts by Mixture of Unigrams and Its Deep
Extensions
c(
person("Martin", "D'Ippolito", email = "martinmy69@gmail.com", role = c("aut","cre")),
person("Anderlucci", "Laura", email = "laura.anderlucci@unibo.it", role = "aut"),
person("Cinzia", "Viroli", email = "cinzia.viroli@unibo.it", role = "aut"))
Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). <doi:10.1023/A:1007692713085>), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) <doi:10.1007/s11634-020-00399-3>) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) <arXiv:1902.06615v2>). There are also functions for graphical representation of clusters obtained.
skmeans, extraDistr, dplyr, Rfast, entropy, ggplot2,
RColorBrewer, stats, graphics, MASS
Martin D'Ippolito [aut, cre],
Anderlucci Laura [aut],
Cinzia Viroli [aut]