Packaged: 2020-04-09 01:04:26 UTC; wanchuangzhu

Description: 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.
Packaged: 2021-04-14 10:36:18 UTC; wanchuangzhu

Description: 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".
Packaged: 2021-04-15 05:50:28 UTC; wanchuangzhu

Imports: ExtMallows, mc2d, PerMallows, Rcpp, stats
Packaged: 2021-04-28 11:32:05 UTC; wanchuangzhu

Imports: ExtMallows, mc2d, PerMallows, rankdist, Rcpp, stats
Packaged: 2021-05-06 13:18:03 UTC; wanchuangzhu

Title: Generate Heatmaps Based on Partitioning Around Medoids (PAM)
Authors@R: c(person("Vidal", "Fey", email = "vidal.fey@gmail.com", role=c("aut", "cre")), person("Henri", "Sara", email = "henri.sara@gmail.com", role=c("aut")))
Description: Data are partitioned (clustered) into k clusters "around medoids", which is
a more robust version of K-means implemented in the function pam() in the 'cluster' package.
The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>.
Please refer to the pam() function documentation for more references.
Clustered data is plotted as a split heatmap allowing visualisation of representative
"group-clusters" (medoids) in the data as separated fractions of the graph while those
"sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.
Depends: heatmapFlex, cluster, grDevices, graphics, stats
Imports: RColorBrewer, R.utils, readxl, readmoRe, utils, plyr, robustHD