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) <doi:10.1007/s11222-020-09989-9>). There are also functions for graphical representation of clusters obtained.
skmeans, extraDistr, dplyr, Rfast, entropy, ggplot2, graphics,
MASS

c(person("Benjamin", "Taylor", , "b.taylor1@lancaster.ac.uk", role = c("aut","cre")))
Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.

c(person("Benjamin", "Taylor", , "benjamin.taylor.software@gmail.com", role = c("aut","cre")))

Daniel Marcous [aut, cre], Yotam Sandbank [aut], Google Inc. [cph]
c(
person("Daniel", "Marcous", email = "dmarcous@gmail.com", role = c("aut","cre")),
person("Yotam", "Sandbank", email = "yotamsandbank@gmail.com", role = "aut"),
person("Google Inc.", role = "cph")
)
Provides deep boosting models training, evaluation, predicting and
hyper parameter optimising using grid search and cross validation.
Based on Google's Deep Boosting algorithm, and Google's C++ implementation.
Cortes, C., Mohri, M., & Syed, U. (2014) <URL:
http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>.