methods,
pracma
Suggests:
+ devtools,
flexmix,
parallel,
testthat,
#' @useDynLib morpheus
#'
#' @importFrom jointDiag ajd
-#' @importFrom stats rbinom rmultinom rnorm pnorm runif integrate
-#' @importFrom graphics boxplot barplot hist par
+#' @importFrom stats rbinom rmultinom rnorm pnorm runif integrate sd
+#' @importFrom graphics boxplot barplot hist par matplot
#' @importFrom methods new
#' @importFrom pracma integral
#'
#' @param prepareArgs Prepare arguments for the functions inside estimParams
#' @param N Number of runs
#' @param ncores Number of cores for parallel runs (<=1: sequential)
+#' @param agg Aggregation method (default: lapply)
#' @param verbose TRUE to indicate runs + methods numbers
#'
#' @return A list of nf aggregates of N results (matrices).
M <- computeMoments(optargs$X,optargs$Y)
}
- # TODO: field?!
- exactComp <<- optargs$exact
- if (is.null(exactComp))
- exactComp <<- FALSE
-
# Build and return optimization algorithm object
methods::new("OptimParams", "li"=link, "M1"=as.double(M[[1]]),
"M2"=as.double(M[[2]]), "M3"=as.double(M[[3]]), "K"=as.integer(K))
# link="probit"; order=2; λ=c(531.8099,586.8893,523.5816); b=c(-118.512674,-3.488020,2.109969)
# Switch to pracma package for that (but it seems slow...)
+ exactComp <- FALSE #TODO: global, or argument...
+
if (exactComp && link == "probit")
{
# Use exact computations
#' Draw 3D map of objective function values
#'
#' @param N Number of starting points
+#' @param n Number of points in sample
+#' @param p Vector of proportions
+#' @param b Vector of biases
#' @param β Regression matrix (target)
#' @param link Link function (logit or probit)
#'
library(testthat)
-#library(morpheus)
-load_all()
+
+# Locally:
+#library(devtools)
+#load_all("../")
+
+# With R CMD check:
+library(morpheus)
test_check("morpheus")
if [ -f $file ]; then
sed -i 's/μ/mu/g' $file
sed -i 's/β/beta/g' $file
+ sed -i 's/ρ/rho/g' $file
sed -i 's/λ/lambda/g' $file
sed -i 's/Σ/Sigma/g' $file
fi