A penalized regression framework that can simultaneously estimate 
    the optimal treatment strategy and identify important variables. 
    Appropriate for either censored or uncensored continuous response.

An implementation of several functions for feature extraction in 
    ordinal time series datasets. Specifically, some of the features proposed by
    Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed.  
    These features can be used to perform inferential tasks or to feed machine
    learning algorithms for ordinal time series, among others. The package also includes some
    interesting datasets containing financial time series. Practitioners from a 
    broad variety of fields could benefit from the general framework provided 
    by 'otsfeatures'.

An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports 
    grading R scripts, R Markdown documents, and R Jupyter Notebooks.

Tools for converting Open-Source Tools for Training Resources
    (OTTR) courses into Leanpub or Coursera courses. 'ottrpal' is for use
    with the OTTR Template repository to create courses.

Summarizes the taxonomic composition, diversity contribution of the rare and abundant community by using OTU (operational taxonomic unit) table which was generated by analyzing pipeline of 'QIIME' or 'mothur'. The rare biosphere in this package is subset by the relative abundance threshold (for details about rare biosphere please see Lynch and Neufeld (2015) <doi:10.1038/nrmicro3400>).

Analyses of OTU tables produced by 16S rRNA gene amplicon sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2017) "Bacterial community composition and dynamics spanning five years in freshwater bog lakes," <doi: 10.1128/mSphere.00169-17>.

Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.

The Open University Learning Analytics Dataset (OULAD) is available from 
    Kuzilek et al. (2017) <doi:10.1038/sdata.2017.171>.  The 'ouladFormat' 
    package loads, cleans and formats the OULAD for data analysis (each row of the
    returned data set is an individual student). The package’s main function,
    combined_dataset(), allows the user to choose whether the returned
    data set includes assessment, demographics, virtual learning environment (VLE),
    or registration variables etc.