Logger to keep track of informational events and errors useful for debugging.

The log4r package is meant to provide a fast, lightweight,
  object-oriented approach to logging in R based on the widely-emulated
  'log4j' system and etymology.

Enables users to handle the dataset cleaning for conducting
    specific analyses with the log files from two international educational
    assessments: the Programme for International Student Assessment (PISA,
    <https://www.oecd.org/pisa/>) and the Programme for the International
    Assessment of Adult Competencies (PIAAC,
    <https://www.oecd.org/skills/piaac/>). An illustration of the analyses can be
    found on the LOGAN Shiny app (<https://loganpackage.shinyapps.io/shiny/>) on
    your browser.

Enables researchers to model log-file data from computer-based assessments using machine-learning techniques. It allows researchers to generate new knowledge by comparing the performance of three tree-based classification models (i.e., decision trees, random forest, and gradient boosting) to predict student's outcome. It also contains a set of handful functions for the analysis of the features' influence on the modeling. Data from the Climate control item from the 2012 Programme for International Student Assessment (PISA, <https://www.oecd.org/pisa/>) is available for an illustration of the package's capability. He, Q., & von Davier, M. (2015) <doi:10.1007/978-3-319-19977-1_13> Boehmke, B., & Greenwell, B. M. (2019) <doi:10.1201/9780367816377> .

Methods for fitting log-link GLMs and GAMs to binomial data,
    including EM-type algorithms with more stable convergence properties than standard methods.

Software for computing a log-concave (maximum likelihood) estimator for independent and identically distributed data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).

Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach and Schuhmacher (2014) <doi:10.1214/14-EJS930>.