Provides easy-to-understand and consistent interfaces for
    accessing data on the U.S. Congress. The functions in 'filibustr'
    streamline the process for importing data on Congress into R, removing
    the need to download and work from CSV files and the like. Data
    sources include 'Voteview' (<https://voteview.com/>), the U.S. Senate
    website (<https://www.senate.gov/>), and more.

Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.

Adds distinctive yet unobtrusive geometric patterns where solid 
    color fills are normally used. Patterned figures look just as 
    professional when viewed by colorblind readers or when printed in black 
    and white. The dozen included patterns can be customized in terms of scale,
    rotation, color, fill, line type, and line width. Compatible with the 
    'ggplot2' package as well as 'grid' graphics.

Edit vectors to fill missing values, based on the vector itself.

Average rating and number of votes reported by IMDb for films and shorts with over 100 votes in 2022.  The data are analysed in Chapter 3 of the Book 'Getting (more out of) Graphics' (Antony Unwin, CRC Press 2024).

Generate search filters to query scientific bibliographic sources, 
    such as PubMed and Web of Science, for non-human primate related 
    publications.

Enables filtering datasets by a prior specified identifiers
    which correspond to saved filter expressions.

Tidy tools to apply filter-based supervised feature selection
    methods. These methods score and rank feature relevance using metrics
    such as p-values, correlation, and importance scores (Kuhn and Johnson
    (2019) <doi:10.1201/9781315108230>).