helps identify plausible and replicable structures via k-fold cross validation. The process
maximizes the available data. Also available are functions to determine k by drawing on

Description: Provides functions to identify plausible and replicable factor structures
for a set of variables via k-fold cross validation. The process combines the exploratory
and confirmatory factor analytic approach to scale development
maximizes the available data (Hastie, Tibshirani, & Friedman, 2009) <isbn:978-0-387-21606-5>.
Also available are functions to determine k by drawing on power analytic techniques for
covariance structures (MacCallum, Browne, & Sugawara, 1996) <doi:10.1037/1082-989X.1.2.130>,
generate model syntax, and summarize results in a report.

Title: Kernel Fisher Discriminant Analysis
Description: Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA).
There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions.
In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly.
This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>.

Title: Integrated Code Chunk Anchoring and Referencing for R Markdown
Description: A streamlined cross-referencing system for R Markdown documents
generated with 'knitr'. R Markdown is  an authoring format for generating
dynamic content from R. 'kfigr' provides a hook for anchoring code
chunks and a function to cross-reference document elements generated from

Maintainer: Michael C Koohafkan <michael.koohafkan@gmail.com>

Title: Kalman Filter for Impulse Noised Outliers
person("Bertrand", "Cloez", email = "bertrand.cloez@inrae.fr", role = c("aut")),
person("Isabelle", "Sanchez", email = "isabelle.sanchez@inrae.fr", role = c("aut", "cre")),
person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = c("ctr")))
Description: A method for detecting outliers with a Kalman filter on impulsed
noised outliers and prediction on cleaned data. 'kfino' is a robust
sequential algorithm allowing to filter data with a large number of outliers.
This algorithm is based on simple latent linear Gaussian processes as in the
Kalman Filter method and is devoted to detect impulse-noised outliers. These
are data points that differ significantly from other observations. 'ML'
(Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm)
algorithms were implemented in 'kfino'. The method is described in full
details in the following arXiv e-Print: <arXiv:2208.00961>.
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), covr, foreach,