Functions for aggregate encoding, dummy encoding,
frequency encoding, label encoding, leave-one-out encoding,
mean encoding, median encoding, and one-hot encoding.
dplyr (>= 0.7.8), purrr, Rcpp, rlang (>= 0.4.2), stats,
tibble, tidyselect (>= 1.0.0)

Functions and S3 classes for the following
methods of encoding categorical features as numerics:
aggregate, dummy, frequency, label, leave-one-out,
mean, median, and one-hot.

Robust Principal Component Analysis Using the Cauchy
Distribution
c( person("Michail", "Tsagris", role = c("aut", "cre"), email = "mtsagris@uoc.gr"),
person("Aisha", "Fayomi", role = c("ctb"), email = "afayomi@kau.edu.sa"),
person("Yannis", "Pantazis", role = c("ctb"), email = "pantazis@iacm.forth.gr"),
person("Andrew T.A.", "Wood", role = c("ctb"), email = "Andrew.Wood@anu.edu.au") )
Michail Tsagris [aut, cre],
Aisha Fayomi [ctb],
Yannis Pantazis [ctb],
Andrew T.A. Wood [ctb]
doParallel, foreach, parallel, Rfast, stats
A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2022). Cauchy robust principal component analysis with applications to high-dimensional data sets. <arXiv:2211.03181>.

Accelerated Bayesian Analytics with DAGs
c(person("Adam", "Fleischhacker", email = "ajf@udel.edu", role = c("aut", "cre","cph")),
person("Daniela", "Dapena", role = "ctb"),
person("Rose", "Nguyen", role = "ctb"),
person("Jared", "Sharpe", role = "ctb")
)
Accelerate Bayesian analytics workflows in 'R' through interactive modelling,
visualization, and inference. Define probabilistic graphical models using directed
acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians,
and programmers. This package relies on the sleek and elegant 'greta' package for
Bayesian inference. 'greta', in turn, is an interface into 'TensorFlow' from 'R'. See
<http://github.com/flyaflya/causact> or <http://causact.com> for more documentation.
Python and TensorFlow are needed for Bayesian
inference computations; Python (>= 2.7.0) with header files and
shared library; TensorFlow (v1.14;
https://www.tensorflow.org/); TensorFlow Probability (v0.7.0;
https://www.tensorflow.org/probability/)Encoding: UTF-8
Adam Fleischhacker [aut, cre, cph],
Daniela Dapena [ctb],
Rose Nguyen [ctb],
Jared Sharpe [ctb]