Projection Pursuit for Cluster Identification
David Hofmeyr <dhofmeyr@sun.ac.za> [aut, cre]
Nicos Pavlidis <n.pavlidis@lancaster.ac.uk> [aut]
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>.

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) <http://jmlr.org/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>.

Prognostic Predictive Lasso for Biomarker Selection
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"))
Wencan Zhu [aut, cre],
Celine Levy-Leduc [ctb],
Nils Ternes [ctb]
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).
genlasso, ggplot2, cvCovEst, glmnet, MASS

Probabilistic Two Sample Mendelian Randomization
c(
person(given = "Zhongshang", family = "Yuan", role = c("aut")),
person(given = "Xiang", family = "Zhou", role = c("aut"), email = "xzhousph@umich.edu"),
person(given = "Michael", family = "Kleinsasser", role = c("cre"), email = "mkleinsa@umich.edu"))
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.
Zhongshang Yuan [aut],
Xiang Zhou [aut],
Michael Kleinsasser [cre]