Title: Diffusion-based IRT models for Response and Response Time Data
Description: Package to fit diffusion-based IRT models to response and
response time data. Models are fitted using marginal maximum
likelihood. Parameter restrictions (fixed value and equality
constraints) are possible. In addition, factor scores (person drift
rate and person boundary separation) can be estimated. Model fit
assessment tools are also available. The traditional diffusion model

Title: Diffusion IRT models for Response and Response Time Data

Title: Diffusion IRT Models for Response and Response Time Data
response time data. Models are fit using marginal maximum

Title: Detecting Gene Probes with Different Means or Variances Between
Authors@R: c(person("Xuan", "Li", role=c("aut", "ctb"),
person("Yuejiao", "Fu", role = c("aut", "ctb"),
Depends: R (>= 3.4.0), Biobase, missMethyl, stats
Imports: MASS, graphics, lawstat, methylumi, grDevices
Description: A collection of functions to detect gene probes with different means or variances between two groups in (epi)-genomic data analysis, including classical F test and Levene's test for equal variance, likelihood ratio test for equal mean and equal variance, the wrappers for functions in R Bioconductor package 'missMethyl' (Phipson and Oshlack (2014) <doi:10.1186/s13059-014-0465-4>), the implementation of the methods proposed by Ahn and Wang (2013) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621641/>, and the improved Ahn and Wang's methods that we proposed recently (Qiu and Li et al. (2017) <http://oprscience.com/wp-content/uploads/2016/07/new-score-tests-for-equality-of-variances-in-the-application-of-dna-methylation-data-analysis.pdf>).

Title: Multi-Fidelity Computer Experiments Using the Tuo-Wu-Yu Model
Author: Wenjia Wang, Rui Tuo, and C. F. Jeff Wu
Description: This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution (i.e., the conceptually highest fidelity).