Provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point.

An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.

A collection of methods for automated data cleaning where all actions are logged.

Attempt to repair inconsistencies and missing values in
        data records by using information from valid values and
        validation rules restricting the data.

Apply the Deductive Rational Method to a monthly series of flow or precipitation data to fill in missing data. The method is as described in: Campos, D.F., (1984, ISBN:9686194444).

Contains one main function deduped() which speeds up slow,
    vectorized functions by only performing computations on the unique values
    of the input and expanding the results at the end.

Duplicated data can exist in different rows and columns and user may need to
    treat observations (rows) connected by duplicated data as one observation,
    e.g. companies can belong to one family (and thus: be one company) by sharing
    some telephone numbers. This package allows to find connected rows
    based on data on chosen columns and collapse it into one row.

Explore neural networks in a layer oriented way, the framework
    is intended to give the user total control of the
    internals of a net without much effort. Use classes like PerceptronLayer
    to create a layer of Percetron neurons, and specify how many you want. The
    package does all the tricky stuff internally leaving you focused in what
    you want. I wrote this package during a neural networks course to help me
    with the problem set.