Packaged: Thu Dec  6 22:12:12 2007; John

symmetry, uniform association. `mclgen' restructures a
dataframe to enable the estimation of a multinomial logistic
model using the conditional logit program `clogit'. This allows
greater flexibility in imposing constraints on the response
variable. One application is to specify aforementioned models
for square tables as multinomial logistic models with
covariates at the respondent level. `ctab' simplifies the
production of (multiway( percentage tables.

Description: `ctab' creates (multiway) percentage tables. `sqtab'
contains a set of functions for estimating models for square
tables such as quasi-independence, symmetry, uniform
association. Examples show how to use these models in a
loglinear model using glm or in a multinomial logistic model

Description: Functions for dummy encoding, frequency encoding,
label encoding, leave-one-out encoding, mean encoding,
Imports: rlang (>= 0.1.6), stats, tidyselect (>= 0.2.3)
Suggests: covr, dplyr, knitr, nycflights13, ranger, rmarkdown,

Imports: dplyr (>= 0.7.0), stats, tidyselect (>= 0.2.3)
Suggests: covr, knitr, nycflights13, ranger, rmarkdown, testthat,

label encoding, leave-one-out encoding, mean encoding, median
Imports: dplyr (>= 0.7.0), purrr, Rcpp, rlang (>= 0.4.0), stats,
Suggests: covr, knitr, lintr, nycflights13, ranger, rmarkdown,
Author: Bernie Gray [aut, cre] (<https://orcid.org/0000-0001-9190-6032>),

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

Description: Functions and S3 classes for the following
methods of encoding categorical features as numerics:
aggregate, dummy, frequency, label, leave-one-out,
Imports: dplyr (>= 1.0.0), purrr, rlang (>= 0.4.6), stats, tibble (>=

Title: Robust Principal Component Analysis Using the Cauchy
Authors@R: 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") )
Imports: doParallel, foreach, parallel, Rfast, stats
Description: 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>.