This SVG elements generator can easily generate 
    SVG elements such as rect, line, circle, ellipse, polygon, 
    polyline, text and group. Also, it can combine and 
    output SVG elements into a SVG file.

Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.

Functions are provided to determine production frontiers and technical 
    efficiency measures through non-parametric techniques based upon regression trees. 
    The package includes code for estimating radial input, output, directional and 
    additive measures, plotting graphical representations of the scores and the production 
    frontiers by means of trees, and determining rankings of importance of input variables 
    in the analysis. Additionally, an adaptation of Random Forest by a set of individual 
    Efficiency Analysis Trees for estimating technical efficiency is also included. More 
    details in: <doi:10.1016/j.eswa.2020.113783>.

Provides simple functions to create constraints for small test assembly problems 
    (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, 
    'GLPK', 'lpSolve', 'Symphony', and 'Gurobi' are supported as solvers. The 'gurobi' package is not available from 
    any mainstream repository; see <https://www.gurobi.com/downloads/>.

Use 'SQLite3' as a database system via a complete SQL free R interface, treating the data as if it was a single spreadsheet.

Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases.

The univariate statistical quality control tool aims to address measurement error effects when constructing exponentially weighted moving average p control charts. The method primarily focuses on binary random variables, but it can be applied to any continuous random variables by using sign statistic to transform them to discrete ones. With the correction of measurement error effects, we can obtain the corrected control limits of exponentially weighted moving average p control chart and reasonably adjusted exponentially weighted moving average p control charts. The methods in this package can be found in some relevant references, such as Chen and Yang (2022) <arXiv: 2203.03384>; Yang et al. (2011) <doi: 10.1016/j.eswa.2010.11.044>; Yang and Arnold (2014) <doi: 10.1155/2014/238719>; Yang (2016) <doi: 10.1080/03610918.2013.763980> and Yang and Arnold (2016) <doi: 10.1080/00949655.2015.1125901>.