Packaged: Tue Apr 17 17:36:07 2007; leiwu

Title: Multiple Administrations Adaptive Testing
Description: Provides an extension of the shadow-test approach to computerized adaptive
testing (CAT) implemented in the 'TestDesign' package for the assessment framework
involving multiple tests administered periodically throughout the year. This framework
is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports
multiple item pools vertically scaled and multiple phases (stages) of CAT within each test.
Between phases and tests, transitioning from one item pool (and associated constraints)
to another is allowed as deemed necessary to enhance the quality of measurement.
Imports: TestDesign (>= 1.2.0), readxl, Rdpack, methods, MASS, diagram
Suggests: testthat (>= 3.0.0), rmarkdown, knitr, kableExtra

Imports: TestDesign (>= 1.3.3), readxl, methods, MASS, diagram

Title: Maximum Approximate Bernstein Likelihood Estimation
Description: Fit raw or grouped continuous data from a population with a smooth density on unit interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>.

Title: Maximum Approximate Bernstein/Beta Likelihood Estimation
Description: Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>. For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.
Imports: survival, graphics, stats, icenReg, aftgee
Suggests: mixtools, ICsurv, interval, knitr, rmarkdown