Rank Aggregation with Partition Mallows Model
person(given = "Wanchuang",
family = "Zhu",
role = c("cre","aut"),
email = "andy.chou.sub@gmail.com")
Rank aggregation aims to achieve a better ranking list given multiple observations. 'PAMA' implements Partition-Mallows model for rank aggregation. Both Bayesian inference and Maximum likelihood estimation (MLE) are provided. It can handle partial list as well. When covariates information is available, this package can make inference by incorporating the covariate information. More information can be found in the paper "Integrated Partition-Mallows Model and Its Inference for Rank Aggregation". The paper is not yet published.

person(given = "Wanchuang",
family = "Zhu",
role = c("cre","aut"),
email = "andy.chou.sub@gmail.com"
)

c(person(given = "Wanchuang",
family = "Zhu",
role = c("cre","aut"),
email = "andy.chou.sub@gmail.com"
))
Rank aggregation aims to achieve a better ranking list given multiple observations. 'PAMA' implements Partition-Mallows model for rank aggregation where the rankers' quality are different. Both Bayesian inference and Maximum likelihood estimation (MLE) are provided. It can handle partial list as well. When covariates information is available, this package can make inference by incorporating the covariate information. More information can be found in the paper "Integrated Partition-Mallows Model and Its Inference for Rank Aggregation". The paper is accepted by Journal of the American Statistical Association.

Rank aggregation aims to achieve a better ranking list given multiple observations. 'PAMA' implements Partition-Mallows model for rank aggregation where the rankers' quality are different. Both Bayesian inference and Maximum likelihood estimation (MLE) are provided. It can handle partial list as well. When covariates information is available, this package can make inference by incorporating the covariate information. More information can be found in the paper "Integrated Partition-Mallows Model and Its Inference for Rank Aggregation".