c(
person("Oleg", "Melnikov", role = c("aut", "cre"), email = "XisReal@gmail.com"),
person("Max", "Lee", role = "ctb", email = "max.lee@rice.edu"),
person("Robert", "Abramov", role = "ctb", email = "rha1@rice.edu"),
person("Richard", "Huang", role = "ctb", email = "richard.b.huang@rice.edu"),
person("Liu", "Tong", role = "ctb", email = "tong.liu@rice.edu"),
person("Jake", "Kornblau", role = "ctb", email = "jake.a.kornblau@rice.edu"),
person("Xinnan", "Lu", role = "ctb", email = "xinnan.lu@rice.edu"),
person("Kiryl", "Novikau", role = "ctb", email = "kiryl.novikau@rice.edu"),
person("Tongyue", "Luo", role = "ctb", email = "tongyue.luo@rice.edu"),
person("Le", "You", role = "ctb", email = "le.you@rice.edu"),
person("Jin", "Chen", role = "ctb", email = "jin.chen@rice.edu"),
person("Chengwei", "Ge", role = "ctb", email = "chengwei.ge@rice.edu"),
person("Jiayao", "Huang", role = "ctb", email = "jiayao.huang@rice.edu")
)
Oleg Melnikov [aut, cre],
Max Lee [ctb],
Robert Abramov [ctb],
Richard Huang [ctb],
Liu Tong [ctb],
Jake Kornblau [ctb],
Xinnan Lu [ctb],
Kiryl Novikau [ctb],
Tongyue Luo [ctb],
Le You [ctb],
Jin Chen [ctb],
Chengwei Ge [ctb],
Jiayao Huang [ctb]

General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) <arXiv:quant-ph/0208069>. Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) <arXiv:quant-ph/9804010>, Prisoner's Dilemma (J. Orlin Grabbe 2005) <arXiv:quant-ph/0506219>, Two Person Duel (Flitney and Abbott 2004) <arXiv:quant-ph/0305058>, Battle of the Sexes (Nawaz and Toor 2004) <arXiv:quant-ph/0110096>, Hawk and Dove Game (Nawaz and Toor 2010) <arXiv:quant-ph/0108075>, Newcomb's Paradox (Piotrowski and Sladkowski 2002) <arXiv:quant-ph/0202074> and Monty Hall Problem (Flitney and Abbott 2002) <arXiv:quant-ph/0109035>.

Estimate Quantitative Genetics Parameters from Generalised
Linear Mixed Models
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.