Title: Differential Item Functioning in Generalized Partial Credit
Description: Provides a function to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
Imports: Rcpp (>= 0.12.4), wordcloud, TeachingDemos, cubature, car,
Packaged: 2018-02-12 16:59:27 UTC; schaubergerg

Imports: Rcpp (>= 0.12.4), TeachingDemos, cubature, caret, statmod,

Description: Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.

Title: GPCSIV, Generalized Principal Component of Symbolic Interval
Author: Brahim Brahim and Sun Makosso-Kallyth <sun.makosso-kallyth@crchuq.ulaval.ca>
Description: This package implements an extension of principal component analysis (PCA) tailored to handle multiple data tables. It can handle Big Data in the sense that the variation in massive data can be described by intervals [a, b] and multiple tables.

Title: Apply Gaussian Process in Functional data analysis
Description: Use functional regression as the mean structure and Gaussian Process as the covariance structure.
Packaged: 2013-12-29 20:53:37 UTC; AdminYafeng

Packaged: 2014-01-03 00:18:36 UTC; AdminYafeng

Packaged: 2014-04-28 16:05:33 UTC; AdminYafeng

Title: Gaussian Process Regression for Functional Data Analysis
Author: Jian Qing Shi, Yafeng Cheng, Evandro Konzen
Description: Functionalities for modelling functional data with
multidimensional inputs, multivariate functional data, and non-separable
and/or non-stationary covariance structure of function-valued processes.
In addition, there are functionalities for functional regression models where
the mean function depends on scalar and/or functional covariates and
the covariance structure depends on functional covariates.
The development version of the package can be found on
Imports: Rcpp (>= 1.0.2), splines, mgcv, MASS, mvtnorm, fields, interp,
stats, graphics, grDevices, fda, fda.usc