A flexible and interfaced framework for importing, processing and ploting Applied Biosystems data files. Application to Reverse-Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA) gene-expression profiling and classification is illustrated in Mareschal, Ruminy et al (2015) <doi:10.1016/j.jmoldx.2015.01.007>. Gene-fusion detection and Sanger sequencing are illustrated in Mareschal, Palau et al (2021) <doi:10.1182/bloodadvances.2020002517>. Examples are provided for genotyping applications as well.

Provides high-level access to neuroimaging data from standard software packages like 'FreeSurfer' <http://freesurfer.net/> on the level of subjects and groups. Load morphometry data, surfaces and brain parcellations based on atlases. Mask data using labels, load data for specific atlas regions only, and visualize data and statistical results directly in 'R'.

Manages a file system cache. Regular files can be moved or copied to the cache folder. Sub-folders can be created in order to organize the files. Files can be located inside the cache using a glob function. Text contents can be easily stored in and retrieved from the cache using dedicated functions. It can be used for an application or a package, as a global cache, or as a per-user cache, in which case the standard OS user cache folder will be used (e.g.: on Linux $HOME/.cache/R/my_app_or_pkg_cache_folder).

Automated feature selection using variety of models
        provided by 'caret' package.
        This work was funded by Poland-Singapore bilateral cooperation
        project no 2/3/POL-SIN/2012.

FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints.

Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis
    (FSDA)' which is comprehensive and computationally efficient
    software package for robust statistics in regression, multivariate
    and categorical data analysis. The current R version implements tools
    for regression: (forward search, S- and MM-estimation, least trimmed
    squares (LTS) and least median of squares (LMS)), for multivariate analysis
    (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression.
    The distinctive feature of our package is the possibility of
    monitoring the statistics of interest as a function of breakdown point,
    efficiency or subset size, depending on the estimator. This is
    accompanied by a rich set of graphical features, such as dynamic
    brushing, linking, particularly useful for exploratory data analysis.