Package providing a number of functions for working with Two- and
Four-parameter Beta and closely related distributions (i.e., the Gamma and
Binomial distributions), including parameterization in terms of moments,
and fitting of Beta distributions to vectors of values. Also includes
functions for estimating classification accuracy, diagnostic performance,
and consistency, making use of what is generally known as the 'Livingston
and Lewis approach' in the psychometric literature as the foundational
method, which makes extensive use of the Beta distribution. A shiny app is
available, providing a GUI for the Livingston and Lewis approach. For link,
see documentation for the LL.CA() function.
Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>.
Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.
Tharwat (2020) <doi:10.1016/j.aci.2018.08.003>.

Package providing a number of functions for working with Two- and
Four-parameter Beta and closely related distributions (i.e., the Gamma and
Binomial distributions), including parameterization in terms of moments,
and fitting of Beta distributions to vectors of values. Also includes
functions for estimating classification accuracy, diagnostic performance,
and consistency, making use of what is generally known as the 'Livingston
and Lewis approach' in the psychometric literature as the foundational
method, which makes extensive use of the Beta distribution. A shiny app is
available, providing a GUI for the Livingston and Lewis approach for binary
classifications. For link, see documentation for the LL.CA() function.
Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>.
Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.
Tharwat (2020) <doi:10.1016/j.aci.2018.08.003>.

Package providing a number of functions for working with Two- and
Four-parameter Beta and closely related distributions (i.e., the Gamma and
Binomial distributions), including parameterization in terms of moments,
and fitting of Beta distributions to vectors of values. Also includes
functions for estimating classification accuracy, diagnostic performance,
and consistency, making use of what is generally known as the 'Livingston
and Lewis approach' in the psychometric literature as the foundational
method, which makes extensive use of the Beta distribution. A shiny app is
available, providing a GUI for the Livingston and Lewis approach for binary
classifications. For link, see documentation for the LL.CA() function.
Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>.
Lord (1965) <doi:10.1007/BF02289490>.
Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.
Tharwat (2020) <doi:10.1016/j.aci.2018.08.003>.