Imports: ape, data.table(>= 1.13.2), coda, foreach, ggplot2, lamW,

Title: Partition of Variation Variance Component Analysis Method
Description: An implementation of the Partition Of variation (POV) method as
developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in
1993 for the analysis of semiconductor data for hard drive manufacturing.
POV is based on sequential sum of squares and is an exact method that
explains all observed variation. It quantitates both the between and within
factor variation effects and can quantitate the influence of both continuous
Authors@R: person("Paul", "Deen", email = "paulext@gmail.com",

Description: The PP package includes estimation of (MLE and WLE) person
parameters for the 3-PL model (three parameter logistic model)
and the GPCM (generalized partial credit model). The parameters
are estimated under the assumption that the other parameters
are known and fixed. The PP package also provides two functions
to estimate person parameters for the 1-PL (Rasch Model). The
package could be useful when items from an itempool/itembank
with known item parameters are administered to a new population
of testtakers and an ability estimation for every testtaker is

Description: The PP package includes estimation of (MLE and WLE) person parameters for the 3-PL model (three parameter logistic model) and the GPCM (generalized partial credit model). The parameters are estimated under the assumption that the other parameters are known and fixed. The PP package also provides two functions to estimate person parameters for the 1-PL (Rasch Model). The package could be useful when items from an itempool/itembank with known item parameters are administered to a new population of testtakers and an ability estimation for every testtaker is needed.

Description: The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
person parameters for the 1,2,3,4-PL model and the GPCM (generalized
partial credit model). The parameters are estimated under the assumption
that the item parameters are known and fixed. The package is useful e.g. in
the case that items from an item pool / item bank with known item parameters
are administered to a new population of test-takers and an ability
estimation for every test-taker is needed.