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## Generated by RcppR6: do not edit by hand
## Version: 0.2.4
## Hash: b10ce6deb0f175b48d92144f81638cc9
##' @importFrom Rcpp evalCpp
##' @importFrom R6 R6Class
##' @useDynLib glmmsr
NULL
##' A vector of terms in the factorization of a graphical model,
##' of mixed continuous types.
##' @keywords internal
`continuous_beliefs` <- function() {
continuous_beliefs__ctor()
}
.R6_continuous_beliefs <-
R6::R6Class(
"continuous_beliefs",
inherit=,
portable=TRUE,
public=list(
.ptr=NULL,
initialize = function(ptr) {
self$.ptr <- ptr
},
append_glmm_belief = function(items, X, Zt, Lambdat, Lind, response, weights) {
continuous_beliefs__append_glmm_belief(self, items, X, Zt, Lambdat, Lind, response, weights)
},
append_normal_belief = function(items, mean, precision) {
continuous_beliefs__append_normal_belief(self, items, mean, precision)
},
size = function() {
continuous_beliefs__size(self)
}),
active=list())
##' Parameters needed to calibrate the cluster tree
##' @keywords internal
`calibration_parameters` <- function() {
calibration_parameters__ctor()
}
.R6_calibration_parameters <-
R6::R6Class(
"calibration_parameters",
inherit=,
portable=TRUE,
public=list(
.ptr=NULL,
initialize = function(ptr) {
self$.ptr <- ptr
}),
active=list(
theta = function(value) {
if (missing(value)) {
calibration_parameters__theta__get(self)
} else {
calibration_parameters__theta__set(self, value)
}
},
beta = function(value) {
if (missing(value)) {
calibration_parameters__beta__get(self)
} else {
calibration_parameters__beta__set(self, value)
}
},
family = function(value) {
if (missing(value)) {
calibration_parameters__family__get(self)
} else {
calibration_parameters__family__set(self, value)
}
},
link = function(value) {
if (missing(value)) {
calibration_parameters__link__get(self)
} else {
calibration_parameters__link__set(self, value)
}
},
n_sparse_levels = function(value) {
if (missing(value)) {
calibration_parameters__n_sparse_levels__get(self)
} else {
calibration_parameters__n_sparse_levels__set(self, value)
}
},
n_quadrature_points = function(value) {
if (missing(value)) {
calibration_parameters__n_quadrature_points__get(self)
} else {
calibration_parameters__n_quadrature_points__set(self, value)
}
}))
##' The beliefs for the clusters and sepsets of a cluster tree,
##' of mixed continuous types.
##' @param beliefs the vector of continuous beliefs to put on the cluster tree
##' @keywords internal
`cluster_graph` <- function(beliefs) {
cluster_graph__ctor(beliefs)
}
.R6_cluster_graph <-
R6::R6Class(
"cluster_graph",
inherit=,
portable=TRUE,
public=list(
.ptr=NULL,
initialize = function(ptr) {
self$.ptr <- ptr
},
compute_log_normalizing_constant = function(mean, precision, parameters) {
cluster_graph__compute_log_normalizing_constant(self, mean, precision, parameters)
}),
active=list(
width = function(value) {
if (missing(value)) {
cluster_graph__width__get(self)
} else {
stop("cluster_graph$width is read-only")
}
}))
`extended_family` <- function(family, link) {
extended_family__ctor(family, link)
}
.R6_extended_family <-
R6::R6Class(
"extended_family",
inherit=,
portable=TRUE,
public=list(
.ptr=NULL,
initialize = function(ptr) {
self$.ptr <- ptr
},
evaluate = function(linear_predictor, response, weights) {
extended_family__evaluate(self, linear_predictor, response, weights)
},
evaluate_d1 = function(linear_predictor, response, weights) {
extended_family__evaluate_d1(self, linear_predictor, response, weights)
},
evaluate_d2 = function(linear_predictor, response, weights) {
extended_family__evaluate_d2(self, linear_predictor, response, weights)
},
evaluate_d3 = function(linear_predictor, response, weights) {
extended_family__evaluate_d3(self, linear_predictor, response, weights)
},
evaluate_d4 = function(linear_predictor, response, weights) {
extended_family__evaluate_d4(self, linear_predictor, response, weights)
}),
active=list())
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