This is a thin wrapper around the 'MOEX' 'ISS' REST interface, see
    <https://iss.moex.com>.  It allows to quickly fetch price candles for a 
    particular security, obtain its profile information and so on.

Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Xiao, Joseph, and Ray (2022) <doi:10.1080/00401706.2022.2141897> proposed Maximum One-Factor-at-a-Time (MOFAT) designs for doing this. A MOFAT design can be viewed as an improvement to the random one-factor-at-a-time (OFAT) design proposed by Morris (1991) <doi:10.1080/00401706.1991.10484804>. The improvement is achieved by exploiting the connection between Morris screening designs and Monte Carlo-based Sobol' designs, and optimizing the design using a space-filling criterion. This work is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921646>.

Functions for exploring the best subsets in regression with a genetic algorithm. The package is much faster than methods relying on complete enumeration, and is suitable for data sets with large number of variables. For more information, see Sinha, Malo & Kuosmanen (2015) <doi:10.1080/10618600.2014.899236>.

Support JSON flattening in a long data frame way, where the nesting keys will be stored in the absolute path. It also
   provides an easy way to summarize the basic description of a JSON list. The idea of 'mojson' is to transform a JSON object in an 
   absolute serialization way, which means the early key-value pairs will appear in the heading rows of the resultant data frame. 
   'mojson' also provides an alternative way of comparing two different JSON lists, returning the left/inner/right-join style results.

Contains functions for performing Mokken
   scale analysis on test and questionnaire data.
   It includes an automated item selection algorithm, and various checks of model assumptions.