Provides a flexible container to transport and manipulate complex
    sets of data. These data may consist of multiple data files and associated
    meta data and ancillary files. Individual data objects have associated system
    level meta data, and data files are linked together using the OAI-ORE standard
    resource map which describes the relationships between the files. The OAI-
    ORE standard is described at <https://www.openarchives.org/ore/>. Data packages
    can be serialized and transported as structured files that have been created
    following the BagIt specification. The BagIt specification is described at
    <https://tools.ietf.org/html/draft-kunze-bagit-08>.

A framework to help construct R data packages in a
    reproducible manner. Potentially time consuming processing of raw data
    sets into analysis ready data sets is done in a reproducible manner
    and decoupled from the usual 'R CMD build' process so that data sets
    can be processed into R objects in the data package and the data
    package can then be shared, built, and installed by others without the
    need to repeat computationally costly data processing.  The package
    maintains data provenance by turning the data processing scripts into
    package vignettes, as well as enforcing documentation and version
    checking of included data objects. Data packages can be version
    controlled on 'GitHub', and used to share data for manuscripts,
    collaboration and reproducible research.

RStudio addins and R functions that make copy-pasting vectors and tables to text painless.

Efficiently and flexibly preprocess data using a set of data filtering, deletion, and interpolation tools.
    These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation and grouping.
    Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data.
    They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods.
    Auto-identification of consecutive NA via run-length based grouping is used in observation deletion, outlier removal, and NA interpolation;
    thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed.
    Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation.
    These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>.