Reads and writes CSV with selected conventions.
 Uses the same generic function for reading and writing to promote consistent formats.

Functions for loading large (10M+ lines) CSV
    and other delimited files, similar to read.csv, but typically faster and
    using less memory than the standard R loader. While not entirely general,
    it covers many common use cases when the types of columns in the CSV file
    are known in advance. In addition, the package provides a class 'int64',
    which represents 64-bit integers exactly when reading from a file. The
    latter is useful when working with 64-bit integer identifiers exported from
    databases. The CSV file loader supports common column types including
    'integer', 'double', 'string', and 'int64', leaving further type
    transformations  to the user.

Provide functions for reading and writing CSVW - i.e. CSV tables and JSON metadata.
    The metadata helps interpret CSV by setting the types and variable names.

Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV.

Processes survey data and displays estimation results along with the relative standard error in a table, including the number of samples and also uses a t-distribution approach to compute confidence intervals, similar to 'SPSS' (Statistical Package for the Social Sciences) software.

An interactive application for working with contingency Tables. The application has a template for solving contingency table problems like chisquare test of independence,association plot between two categorical variables. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/CategoricalDataAnalysis/>.

Clean, decompose and aggregate univariate time series following the procedure "Cyclic/trend decomposition using bin interpolation" and the Logbox method for flagging outliers, both detailed in Ritter, F.: Technical note: A procedure to clean, decompose, and aggregate time series, Hydrol. Earth Syst. Sci., 27, 349–361, <doi:10.5194/hess-27-349-2023>, 2023.