Nothing
#' Additive and Multiplicative Effects Models for Networks and Relational Data
#'
#' Analysis of network and relational data using additive and multiplicative
#' effects (AME) models. The basic model includes regression terms,
#' the covariance structure of the social relations model
#' (Warner, Kenny and Stoto (1979), Wong (1982)), and multiplicative
#' factor effects (Hoff(2009)). Four different link functions accommodate
#' different relational data structures, including binary/network data (bin),
#' normal relational data (nrm), ordinal relational data (ord) and data from
#' fixed-rank nomination schemes (frn). Several of these link functions are
#' discussed in Hoff, Fosdick, Volfovsky and Stovel (2013). Development of this
#' software was supported in part by NICHD grant R01HD067509.
#'
#' \tabular{ll}{ Package: \tab amen\cr Type: \tab Package\cr Version: \tab
#' 1.4.4 \cr Date: \tab 2020-12-01 \cr License: \tab GPL-3 \cr }
#'
#' @name amen-package
#' @aliases amen-package amen
#' @author Peter Hoff, Bailey Fosdick, Alex Volfovsky, Yanjun He
#'
#' Maintainer: Peter Hoff <peter.hoff@@duke.edu>
#' @keywords package
#' @examples
#'
#'
#' data(YX_frn)
#' fit<-ame(YX_frn$Y,YX_frn$X,burn=5,nscan=5,odens=1,family="frn")
#'
#' summary(fit)
#'
#' plot(fit)
#'
#' @import stats
#' @import graphics
"_PACKAGE"
#' binary relational data and covariates
#'
#' a synthetic dataset that includes binary relational data as well as
#' information on eight covariates
#'
#'
#' @name YX_bin
#' @docType data
#' @usage data(YX_bin)
#' @format The format is: List of 2 $ Y: num [1:100, 1:100] NA 0 0 0 0 0 0 0 0
#' 1 ... $ X: num [1:100, 1:100, 1:8] 1 1 1 1 1 1 1 1 1 1 ... ..- attr(*,
#' "dimnames")=List of 3 .. ..$ : NULL .. ..$ : NULL .. ..$ : chr [1:8]
#' "intercept" "rgpa" "rsmoke" "cgpa" ...
#' @keywords datasets
#' @examples
#'
#' data(YX_bin)
#' gofstats(YX_bin$Y)
#'
NULL
#' Censored binary nomination data and covariates
#'
#' a synthetic dataset that includes relational data where the number of
#' nominations per row is censored at 10, along with information on eight
#' covariates
#'
#'
#' @name YX_cbin
#' @docType data
#' @usage data(YX_cbin)
#' @format The format is: List of 2 $ Y: num [1:100, 1:100] NA 0 0 0 1 0 0 0 0
#' 3 ... $ X: num [1:100, 1:100, 1:8] 1 1 1 1 1 1 1 1 1 1 ... ..- attr(*,
#' "dimnames")=List of 3 .. ..$ : NULL .. ..$ : NULL .. ..$ : chr [1:8]
#' "intercept" "rgpa" "rsmoke" "cgpa" ...
#' @keywords datasets
#' @examples
#'
#' data(YX_cbin)
#' gofstats(YX_cbin$Y)
#'
NULL
#' Fixed rank nomination data and covariates
#'
#' a synthetic dataset that includes fixed rank nomination data as well as
#' information on eight covariates
#'
#'
#' @name YX_frn
#' @docType data
#' @usage data(YX_frn)
#' @format The format is: List of 2 $ Y: num [1:100, 1:100] NA 0 0 0 1 0 0 0 0
#' 3 ... $ X: num [1:100, 1:100, 1:8] 1 1 1 1 1 1 1 1 1 1 ... ..- attr(*,
#' "dimnames")=List of 3 .. ..$ : NULL .. ..$ : NULL .. ..$ : chr [1:8]
#' "intercept" "rgpa" "rsmoke" "cgpa" ...
#' @keywords datasets
#' @examples
#'
#' data(YX_frn)
#' gofstats(YX_frn$Y)
#'
NULL
#' normal relational data and covariates
#'
#' a synthetic dataset that includes continuous (normal) relational data as
#' well as information on eight covariates
#'
#'
#' @name YX_nrm
#' @docType data
#' @usage data(YX_nrm)
#' @format The format is: List of 2 $ Y: num [1:100, 1:100] NA -4.05 -0.181
#' -3.053 -1.579 ... $ X: num [1:100, 1:100, 1:8] 1 1 1 1 1 1 1 1 1 1 ... ..-
#' attr(*, "dimnames")=List of 3 .. ..$ : NULL .. ..$ : NULL .. ..$ : chr [1:8]
#' "intercept" "rgpa" "rsmoke" "cgpa" ...
#' @keywords datasets
#' @examples
#'
#' data(YX_nrm)
#' gofstats(YX_nrm$Y)
#'
#'
NULL
#' ordinal relational data and covariates
#'
#' a synthetic dataset that includes ordinal relational data as well as
#' information on seven covariates
#'
#'
#' @name YX_ord
#' @docType data
#' @usage data(YX_ord)
#' @format The format is: List of 2 $ Y: num [1:100, 1:100] NA 0 3 0 3 1 0 1 1
#' 0 ... $ X: num [1:100, 1:100, 1:7] 1 1 1 1 1 1 1 1 1 1 ... ..- attr(*,
#' "dimnames")=List of 3 .. ..$ : NULL .. ..$ : NULL .. ..$ : chr [1:7] "rgpa"
#' "rsmoke" "cgpa" "csmoke" ...
#' @keywords datasets
#' @examples
#'
#' data(YX_ord)
#' gofstats(YX_ord$Y)
#'
NULL
#' row-specific ordinal relational data and covariates
#'
#' a synthetic dataset that includes row-specific ordinal relational data as
#' well as information on five covariates
#'
#'
#' @name YX_rrl
#' @docType data
#' @usage data(YX_rrl)
#' @format The format is: List of 2 $ Y: num [1:100, 1:100] NA 0 3 0 3 1 0 1 1
#' 0 ... $ X: num [1:100, 1:100, 1:5] 1 1 1 1 1 1 1 1 1 1 ... ..- attr(*,
#' "dimnames")=List of 3 .. ..$ : NULL .. ..$ : NULL .. ..$ : chr [1:5] "cgpa"
#' "csmoke" "igrade" "ismoke" ...
#' @keywords datasets
#' @examples
#'
#' data(YX_rrl)
#' gofstats(YX_rrl$Y)
#'
NULL
#' binary relational data and covariates
#'
#' a synthetic dataset that includes longitudinal binary relational data
#' as well as information on covariates
#'
#'
#' @name YX_bin_long
#' @docType data
#' @usage data(YX_bin_long)
#' @format a list
#' @keywords datasets
#' @examples
#'
#' data(YX_bin_long)
#' gofstats(YX_bin_long$Y[,,1])
#'
NULL
#' @title Sampson's monastery data
#'
#' @description
#' Several dyadic variables measured on 18 members of a monastery.
#'
#' @format
#' A socioarray whose dimensions represent nominators, nominatees and relations.
#' Each monk was asked to rank up to three other monks on a variety of positive
#' and negative relations. A rank of three indicates the "highest" ranking for
#' a particular relational variable. The relations \code{like_m2} and \code{like_m1}
#' are evaluations of likeing at one and two timepoints previous to when the
#' other relations were measured.
#'
#' @source Linton Freeman
#'
#' @name sampsonmonks
NULL
#' @title Cold War data
#'
#' @description
#' Positive and negative relations between countries during the cold war
#'
#' @format
#' A list including the following dyadic and nodal variables:
#' \itemize{
#' \item \code{cc}: a socioarray of ordinal levels of military
#' cooperation (positive) and conflict (negative), every 5 years;
#' \item \code{distance}: between-country distance (in thousands of kilometers);
#' \item \code{gdp}: country gdp in dollars every 5 years;
#' \item \code{polity}: country polity every 5 years.
#' }
#' @source
#' Xun Cao : \url{https://polisci.la.psu.edu/people/xuc11/}
#'
#' @name coldwar
NULL
#' @title Comtrade data
#'
#' @description
#' Eleven years of import and export data between 229 countries.
#' The data use the SITC Rev. 1 commodity classification, aggregated at the
#' first level (AG1).
#'
#' @format A list consisting of a socioarray \code{Trade} and a vector
#' \code{dollars2010} of inflation rates. The socioarray gives
#' yearly trade volume (exports and imports)
#' in dollars for 10 different commodity classes
#' for eleven years between 229 countries. This gives a five-way
#' array. The first index is the reporting country, so
#' \code{Trade[i,j,t,k,1]} is what \code{i} reports for exports to
#' \code{j}, but in general this is not the same as
#' \code{Trade[j,i,t,k,2]}, what \code{j} reports as importing from \code{i}.
#'
#' @source \url{https://comtrade.un.org}, \url{https://www.measuringworth.com}
#'
#' @name comtrade
NULL
#' @title Lazega's law firm data
#'
#' @description
#' Several nodal and dyadic variables measured on 71 attorneys in a law firm.
#'
#' @format
#' A list consisting of a socioarray \code{Y} and a nodal attribute matrix \code{X}.
#'
#' The dyadic variables in \code{Y} include three binary networks: advice, friendship
#' and co-worker status.
#'
#' The categorical nodal attributes in \code{X} are coded as follows:
#' \itemize{
#' \item status (1=partner, 2=associate)
#' \item office (1=Boston, 2=Hartford, 3=Providence)
#' \item practice (1=litigation, 2=corporate)
#' \item law school (1=Harvard or Yale, 2=UConn, 3=other)
#' }
#' \code{seniority} and \code{age} are given in years, and \code{female} is
#' a binary indicator.
#'
#' @source Linton Freeman
#'
#' @name lazegalaw
NULL
#' AddHealth community 3 data
#'
#' A valued sociomatrix (Y) and matrix of nodal attributes (X) for
#' students in community 3 of the AddHealth study.
#' \itemize{
#' \item Y: A sociomatrix in which the value of the edge corresponds to an ad-hoc measure of intensity of the relation. Note that students were only allowed to nominate up to 5 male friends and 5 female friends.
#' \item X: Matrix of students attributes, including sex, race (1=white, 2=black, 3=hispanic, 4=asian, 5=mixed/other) and grade.
#' }
#' @docType data
#' @keywords datasets
#' @format list
#' @name addhealthc3
#' @usage data(addhealthc3)
NULL
#' AddHealth community 9 data
#'
#' A valued sociomatrix (Y) and matrix of nodal attributes (X) for
#' students in community 9 of the AddHealth study.
#' \itemize{
#' \item Y: A sociomatrix in which the value of the edge corresponds to an ad-hoc measure of intensity of the relation. Note that students were only allowed to nominate up to 5 male friends and 5 female friends.
#' \item X: Matrix of students attributes, including sex, race (1=white, 2=black, 3=hispanic, 4=asian, 5=mixed/other) and grade.
#' }
#' @docType data
#' @keywords datasets
#' @format list
#' @name addhealthc9
#' @usage data(addhealthc9)
NULL
#' @title International relations in the 90s
#'
#' @description
#' A relational dataset recording
#' a variety of nodal and dyadic variables on countries in the 1990s,
#' including information on conflicts, trade and other variables.
#' Except for the conflict variable, the variables are averages
#' across the decade.
#'
#' @format
#' A list consisting of a socioarray \code{dyadvars} of
#' dyadic variables and matrix \code{nodevars} of nodal variables.
#' The dyadic variables include
#' \itemize{
#' \item total number of conflicts;
#' \item exports (in billions of dollars);
#' \item distance (in thousands of kilometers);
#' \item number of shared IGOs (averages across the years);
#' \item polity interaction.
#' }
#' The nodal variables include
#' \itemize{
#' \item population (in millions);
#' \item gdp (in billions of dollars);
#' \item polity
#' }
#'
#' @source Michael Ward.
#'
#' @name IR90s
NULL
#' @title Dutch college data
#'
#' @description
#' Longitudinal relational measurements and nodal characteristics
#' of Dutch college students, described in
#' van de Bunt, van Duijn, and Snijders (1999).
#' The time interval between the first four measurements was
#' three weeks, whereas the interval between the last three
#' was six weeks.
#'
#' @format A list consisting of a socioarray \code{Y} and a matrix
#' \code{X} of static nodal attributes. The relational
#' measurements range from -1 to 4, indicating the following:
#' \itemize{
#' \item -1 a troubled or negative relationship
#' \item 0 don't know
#' \item 1 neutral relationship
#' \item 2 friendly
#' \item 3 friendship
#' \item 4 best friends
#' }
#'
#' @source Linton Freeman
#'
#' @name dutchcollege
NULL
#' @title Sheep dominance data
#'
#' @description
#' Number of dominance encounters between 28 female bighorn sheep.
#' Cell (i,j) records the number of times sheep i dominated sheep j.
#' From Hass (1991).
#'
#' @format
#' A list consisting of the following:
#' \itemize{
#' \item \code{dom}: a directed socioarray recording the number of
#' dominance encounters.
#' \item \code{age}: the age of each sheep in years.
#' }
#'
#' @source Linton Freeman
#'
#' @name sheep
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.