person("Constantin", "Ahlmann-Eltze", email = "artjom31415@googlemail.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3762-068X"))
The summation notation suggested by Einstein (1916) <doi:10.1002/andp.19163540702> is a
concise mathematical notation that implicitly sums over repeated indices of n-dimensional arrays. Many ordinary
matrix operations (e.g. transpose, matrix multiplication, scalar product, 'diag()', trace etc.)
can be written using Einstein notation. The notation is particularly convenient for
expressing operations on arrays with more than two dimensions because the
respective operators ('tensor products') might not have a standardized name.
Constantin Ahlmann-Eltze [aut, cre]
(<https://orcid.org/0000-0002-3762-068X>)

Access to RFR (Risk-Free Rate) Curves Produced by the EIOPA
person(given = "Mehdi",
family = "Echchelh",
role = c("aut", "cre"),
email = "mehdi.echel@gmail.com",
comment = c())
Provides EIOPA (European Insurance And Occupational Pensions Authority) risk-free rates. Please note that the author of this package is not affiliated with EIOPA. The data is accessed through a REST API available at <https://mehdiechchelh.com/api/>.

Explanatory Item Response Modeling for Dichotomous and
Polytomous Items
person("Okan", "Bulut", email = "bulut@ualberta.ca", role = c("aut", "cre"),
comment = c(ORCID="0000-0001-5853-1267"))
Analysis of dichotomous and polytomous response data using the explanatory item response modeling framework, as described in Stanke & Bulut (2019) <doi:10.21449/ijate.515085> and De Boeck & Wilson (2004) <doi:10.1007/978-1-4757-3990-9>. Generalized linear mixed modeling is used for estimating the effects of item-related and person-related variables on dichotomous and polytomous item responses.

magrittr, shiny, shinydashboard, shinycssloaders, readxl

magrittr, shiny, shinydashboard, shinycssloaders, readxl,
ggeffects, ggplot2

Analysis of dichotomous and polytomous response data using the explanatory item response modeling framework, as described in Bulut, Gorgun, & Yildirim-Erbasli (2021) <doi:10.3390/psych3030023>, Stanke & Bulut (2019) <doi:10.21449/ijate.515085>, and De Boeck & Wilson (2004) <doi:10.1007/978-1-4757-3990-9>. Generalized linear mixed modeling is used for estimating the effects of item-related and person-related variables on dichotomous and polytomous item responses.