that the item parameters are known and fixed. 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

person("Reif", "Manuel", email = "manuel.reif@gmail.com", role = c("cre","aut")),
person("Steinfeld", "Jan", email = "jan.d.steinfeld@gmail.com", role = "aut"))
Suggests: testthat, roxygen2, knitr, eRm, data.table, prettydoc

Jan Steinfeld [cre, aut] (<https://orcid.org/0000-0001-9853-8260>)
Packaged: 2019-04-06 20:29:43 UTC; jansteinfeld

Author: Jan Steinfeld [cre, aut] (<https://orcid.org/0000-0001-9853-8260>),
Suggests: testthat, roxygen2, knitr, eRm, data.table, prettydoc,
Packaged: 2021-05-24 11:23:23 UTC; jansteinfeld

Title: Projection Pursuit for Cluster Identification
Author: David Hofmeyr <dhofmeyr@sun.ac.za> [aut, cre]
Description: Implements recently developed projection
pursuit algorithms for finding optimal linear cluster
separators. The clustering algorithms use optimal
hyperplane separators based on minimum density, Pavlidis et. al (2016) <https://jmlr.csail.mit.edu/papers/volume17/15-307/15-307.pdf>;
minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>;

Title: Prognostic Predictive Lasso for Biomarker Selection
Authors@R: c(person("Wencan", "Zhu", email = "wencan.zhu@agroparistech.fr", role = c("aut", "cre")),
person("Celine","Levy-Leduc", email="celine.levy-leduc@agroparistech.fr", role = "ctb"),
person("Nils", "Ternes", email="Nils.Ternes@sanofi.com", role = "ctb"))
Description: We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper <arXiv:2202.01970> (Zhu et al., 2022).
Imports: genlasso, ggplot2, cvCovEst, glmnet, MASS

Title: Probabilistic Two Sample Mendelian Randomization
person(given = "Zhongshang", family = "Yuan", role = c("aut")),
Description: Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis.
It can account for the correlated instruments and the horizontal pleiotropy,
and can provide the accurate estimates of both causal effect and horizontal
pleiotropy effect as well as the two corresponding p-values. There are two main
functions in the 'PPMR' package. One is PMR_individual() for individual level data,
the other is PMR_summary() for summary data.