Title: alpha-NOMINATE Ideal Point Estimator
Author: Christopher Hare <chare@uga.edu>, Jeffrey Lewis <jblewis@ucla.edu>, Keith Poole <ktpoole@uga.edu>, Howard Rosenthal <hl31@nyu.edu>, Royce Carroll <rcarroll@rice.edu>, James Lo <lo@uni-mannheim.de>
Description: Fits ideal point model described in Carroll, Lewis, Lo, Poole and Rosenthal, "The Structure of Utility in Models of Spatial Voting," American Journal of Political Science 57(4): 1008--1028.
Packaged: 2014-04-13 01:45:17 UTC; Christopher

Author: Royce Carroll <rcarroll@rice.edu>, Christopher Hare <chare@uga.edu>, Jeffrey B. Lewis <jblewis@ucla.edu>, James Lo <lo@uni-mannheim.de>, Keith T. Poole <ktpoole@uga.edu>, and Howard Rosenthal <hl31@nyu.edu>
Packaged: 2014-04-27 04:41:11 UTC; Christopher

Depends: coda, wnominate, oc, pscl, MCMCpack
Packaged: 2014-10-09 01:51:51 UTC; Christopher

Title: Alpha-NOMINATE Ideal Point Estimator
Author: Royce Carroll <r.carroll@essex.ac.uk>, Christopher Hare <cdhare@ucdavis.edu>, Jeffrey B. Lewis <jblewis@ucla.edu>, James Lo <lojames@usc.edu>, Keith T. Poole <ktpoole@uga.edu>, and Howard Rosenthal <hl31@nyu.edu>
Description: Fits ideal point model described in Carroll, Lewis, Lo, Poole and Rosenthal (2013), "The Structure of Utility in Models of Spatial Voting," American Journal of Political Science 57(4): 1008--1028, <doi:10.1111/ajps.12029>.
Depends: stats, coda, wnominate, pscl, MCMCpack
Packaged: 2017-10-08 01:03:03 UTC; Christopher

Title: Anonymize Data Containing Personally Identifiable Information
Description: Allows users to quickly and easily anonymize data containing
Personally Identifiable Information (PII) through convenience functions.
Authors@R: person("Paul", "Hendricks", , "paul.hendricks.2013@owu.edu", c("aut", "cre"))
Packaged: 2015-08-25 16:51:31 UTC; phendricks

Packaged: 2015-09-08 15:15:57 UTC; phendricks

Authors@R: c(person("Philip", "Agnew", email = "philip.agnew@ird.fr", role = c("aut", "cre")),
person("Jimmy","Lopez", email = "jimmy.lopez@ird.fr", role = c("aut")))
Description: Epidemiological population dynamics models traditionally define
a pathogen's virulence as the increase in the per capita rate of mortality
of infected hosts due to infection. This package provides functions
allowing virulence to be estimated by maximum likelihood techniques. The
approach is based on the analysis of relative survival comparing survival
in matching cohorts of infected vs. uninfected hosts (Agnew 2019)
Suggests: knitr, rmarkdown, stats, testthat
Packaged: 2020-10-15 16:10:52 UTC; philipagnew