employment

<p>employment in different countries by gender and status.</p>

data

Methods for analysis of compositional data including robust
methods  (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace
rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>),
count zeros (<doi:10.1177/1471082X14535524>),
methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier
detection for compositional data, (robust) principal component analysis for
compositional data, (robust) factor analysis for compositional data, (robust)
discriminant analysis for compositional data (Fisher rule), robust regression
with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>),
contingency (<doi:10.1080/03610926.2013.824980>)
and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>)
and (robust) Anderson-Darling normality tests for
compositional data as well as popular log-ratio transformations (addLR, cenLR,
isomLR, and their inverse transformations). In addition, visualisation and
diagnostic tools are implemented as well as high and low-level plot functions
for the ternary diagram.

Matthias Templ

robCompositions

Compositional Data Analysis

Karel Hron

Peter Filzmoser

Kamila Facevicova

Petra Kynclova

Jan Walach

Veronika Pintar

Jiajia Chen

Dominika Miksova

Bernhard Meindl

Alessandra Menafoglio

Alessia Di Blasi

Federico Pavone

Nikola Stefelova

Gianluca Zeni

Roman Wiederkehr

employment function

Format

employment in different countries by gender and status. — employment

employment in different countries by gender and status.

employment: employment in different countries by gender and status.

Description

Usage

Arguments

Format

Examples