_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-missmda 1.19
Propagated dependencies: r-mvtnorm@1.3-2 r-mice@3.16.0 r-ggplot2@3.5.1 r-foreach@1.5.2 r-factominer@2.11 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://factominer.free.fr/missMDA/index.html
Licenses: GPL 2+
Synopsis: Handling Missing Values with Multivariate Data Analysis
Description:

Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.

Total results: 1