Test Functions for Simulation Experiments and Evaluating
Optimization and Emulation Algorithms
Test functions are often used to test computer code.
They are used in optimization to test algorithms and in
metamodeling to evaluate model predictions. This package provides
test functions that can be used for any purpose.
Some functions are taken from <https://www.sfu.ca/~ssurjano>, but
their R code is not used.

Optimal Analysis of Test and Rating Scale Data
James Ramsay [aut,cre],
Juan Li [aut,cre]
R (>= 3.5), fda, ggplot2, rgl, knitr, rmarkdown
Develop, evaluate, and score multiple choice examinations,
psychological scales, questionnaires, and similar types of data involving
sequences of choices among one or more sets of answers.
Using the package does not require any formal statistical knowledge
beyond what would be provided by a first course in statistics in a
social science department.  There the user would encounter the concept
of probability and how it is used to model data and make decisions,
and would become familiar with basic mathematical and statistical notation.
The essential aspects of each display were designed to be self-explanatory,
although more statistically sophisticated users will also find information
that they may find helpful. Most of the output is in graphical form. Two
recent papers on the methodology are
Ramsay, James; Li, Juan; Wiberg, Marie (2020) <doi:10.3390/psych2040026> and
Ramsay, James; Wiberg, Marie; Li, Juan (2019) <doi:10.3102/1076998619885636>.
dplyr, ggpubr, stringr, tidyr, pracma, utf8

dplyr, ggpubr, stringr, tidyr, pracma, utf8, knitr, rmarkdown

Develop, evaluate, and score multiple choice examinations,
psychological scales, questionnaires, and similar types of data involving
sequences of choices among one or more sets of answers.
This version of the package should be considered as brand new.  Almost all
of the functions have been changed, including their argument list.
See the file NEWS.Rd in the Inst folder for more information.
Using the package does not require any formal statistical knowledge
beyond what would be provided by a first course in statistics in a
social science department.  There the user would encounter the concept
of probability and how it is used to model data and make decisions,
and would become familiar with basic mathematical and statistical notation.
Most of the output is in graphical form. Two
recent papers on the methodology are
Ramsay, James; Li, Juan; Wiberg, Marie (2020) <doi:10.3390/psych2040026> and
Ramsay, James; Wiberg, Marie; Li, Juan (2019) <doi:10.3102/1076998619885636>.