Tailored explicitly for Experience Sampling Method (ESM)
    data, it contains a suite of functions designed to simplify
    preprocessing steps and create subsequent reporting. It empowers
    users with capabilities to extract critical insights during
    preprocessing, conducts thorough data quality assessments (e.g., design
    and sampling scheme checks, compliance rate, careless responses), and
    generates visualizations and concise summary tables tailored
    specifically for ESM data. Additionally, it streamlines the creation
    of informative and interactive preprocessing reports, enabling
    researchers to transparently share their dataset preprocessing
    methodologies. Finally, it is part of a larger ecosystem which
    includes a framework and a web gallery
    (<https://preprocess.esmtools.com/>).

Exploitation, processing and 2D-3D visualization of DICOM-RT files (structures, dosimetry, imagery) for medical physics and clinical research, in a patient-oriented perspective.

A 'shiny' gadget to create 'ggplot2' figures interactively with drag-and-drop to map your variables to different aesthetics.
    You can quickly visualize your data accordingly to their type, export in various formats,
    and retrieve the code to reproduce the plot.

Simultaneous modeling of the quantile and the expected shortfall of a response variable given 
    a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis,
	     Michael I Jordan (2013) <arXiv:1301.2267>. The ESS algorithm
	     is used for model selection in decomposable graphical models.

Functions for converting objects to scalars (vectors of length 1) 
    and a more inclusive definition of data that can be interpreted as numbers 
    (numeric and complex alike).

Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.