testthat, xml2, randomForest, rpart, RWeka, farff, knitr,
rmarkdown, R.rsp, lintr (>= 1.0.1)
backports (>= 1.1.0), BBmisc (>= 1.11), checkmate (>= 1.8.2),
ParamHelpers (>= 1.10), data.table, digest, httr, stringi, XML,
jsonlite, memoise (>= 1.0.0), stats, curl (>= 3.1)

testthat, mlr (>= 2.15), randomForest, rpart, RWeka, xml2,
farff, knitr, rmarkdown, R.rsp, lintr (>= 1.0.1), rex

Control number of OpenMP threads dynamically
The OpenMPController package provides a function
'omp_set_num_threads()' to set the number of OpenMP threads to
be used.  This may be useful, for example, when linking against
a vendor optimised BLAS/LAPACK library (e.g. the AMD Core Math
Library), since the defaults used by those libraries may not be
highly performant.

The OpenMPController package provides a function 'omp_set_num_threads()'
to set the number of OpenMP threads to be used.
This may be useful, for example, when linking against a vendor
optimised BLAS/LAPACK library (e.g. the AMD Core Math
Library), since the defaults used by those libraries may not
be highly performant.

Control Number of OpenMP Threads Dynamically

The OpenMPController package provides a function omp_set_num_threads()
to set the number of OpenMP threads to be used.
This may be useful, for example, when linking against a vendor
optimised BLAS/LAPACK library (e.g. the AMD Core Math
Library), since the defaults used by those libraries may not
be highly performant.

c(person(c("Steven", "M."), "Boker", role="aut"),
person(c("Michael", "C."), "Neale", role="aut"), person(c("Hermine", "H."),
"Maes", role="aut"), person(c("Michael", "J."), "Wilde", role="ctb"), person("Michael",
"Spiegel", role="aut"), person(c("Timothy", "R."), "Brick", role="aut"),
person("Ryne", "Estabrook", role="aut"), person(c("Timothy", "C."),
"Bates", role="aut"), person("Paras", "Mehta", role="ctb"), person("Timo", c("von",
"Oertzen"), role="ctb"), person(c("Ross", "J."), "Gore", role="aut"),
person(c("Michael", "D."), "Hunter", role="aut"), person(c("Daniel", "C."), "Hackett", role="ctb"),
person("Julian", "Karch", role="ctb"), person(c("Andreas", "M."), "Brandmaier", role="ctb"),
person(c("Joshua", "N."), "Pritikin", email="jpritikin@pobox.com",
role=c("aut", 'cre')), person("Mahsa", "Zahery", role="aut"),
person(c("Robert", "M."), "Kirkpatrick", role="aut"),
person(c("Yang"), "Wang", role="ctb"),
person(c("Charles"), "Driver", email="driver@mpib-berlin.mpg.de", role="ctb"),
person("Massachusetts Institute of Technology", role="cph"),
person("S. G.", "Johnson", role="cph"),
person("Association for Computing Machinery", role="cph"),
person("Dieter", "Kraft", role="cph"),
person("Stefan", "Wilhelm", role="cph"),
person("Manjunath B G", role="cph"))
Facilitates treatment of statistical model specifications
as things that can be generated and manipulated programmatically.
Structural equation models may be specified with reticular action model matrices or paths,
linear structural relations matrices or paths, or
directly in matrix algebra.
Fit functions include full information maximum likelihood,
maximum likelihood, and weighted least squares.
Example models include confirmatory factor, multiple group, mixture
distribution, categorical threshold, modern test theory, differential
equations, state space, and many others.  MacOS users can download the most up-to-date
package binaries from <http://openmx.ssri.psu.edu>.
See Neale, Hunter, Pritikin, Zahery, Brick,
Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.
Rcpp, digest, MASS, methods, Matrix, parallel
mvtnorm, numDeriv, roxygen2 (>= 3.1), devtools, Rmpi, rpf (>=
0.45), snowfall, lme4
'0ClassUnion.R' 'cache.R' 'MxBaseNamed.R' 'MxData.R'
'MxDataWLS.R' 'DefinitionVars.R' 'MxReservedNames.R'
'MxNamespace.R' 'MxSearchReplace.R' 'MxFlatSearchReplace.R'
'MxUntitled.R' 'MxAlgebraFunctions.R' 'MxExponential.R'
'MxMatrix.R' 'DiagMatrix.R' 'FullMatrix.R' 'IdenMatrix.R'
'LowerMatrix.R' 'SdiagMatrix.R' 'StandMatrix.R' 'SymmMatrix.R'
'UnitMatrix.R' 'ZeroMatrix.R' 'MxMatrixFunctions.R'
'MxAlgebra.R' 'MxCycleDetection.R' 'MxDependencies.R'
'MxAlgebraConvert.R' 'MxAlgebraTransform.R' 'MxSquareBracket.R'
'MxEval.R' 'MxRename.R' 'MxPath.R' 'MxObjectiveMetaData.R'
'MxRAMMetaData.R' 'MxExpectation.R' 'MxExpectationNormal.R'
'MxExpectationRAM.R' 'MxExpectationLISREL.R' 'MxFitFunction.R'
'MxFitFunctionAlgebra.R' 'MxFitFunctionML.R'
'MxFitFunctionMultigroup.R' 'MxFitFunctionRow.R'
'MxFitFunctionWLS.R' 'MxRAMObjective.R' 'MxLISRELObjective.R'
'MxFIMLObjective.R' 'MxMLObjective.R' 'MxRowObjective.R'
'MxAlgebraObjective.R' 'MxBounds.R' 'MxConstraint.R'
'MxInterval.R' 'MxTypes.R' 'MxCompute.R' 'MxModel.R'
'MxRAMModel.R' 'MxLISRELModel.R' 'MxModelDisplay.R'
'MxFlatModel.R' 'MxMultiModel.R' 'MxModelFunctions.R'
'MxModelParameters.R' 'MxUnitTesting.R' 'MxApply.R' 'MxRun.R'
'MxRunHelperFunctions.R' 'MxSummary.R' 'MxCompare.R'
'MxSwift.R' 'MxOptions.R' 'MxThreshold.R' 'OriginalMx.R'
'MxGraph.R' 'MxGraphviz.R' 'MxDeparse.R' 'MxCommunication.R'
'MxRestore.R' 'MxVersion.R' 'MxPPML.R' 'MxRAMtoML.R' 'MxDiff.R'
'MxErrorHandling.R' 'MxDetectCores.R' 'MxSaturatedModel.R'
'omxBrownie.R' 'omxConstrainThresholds.R' 'omxGetNPSOL.R'
'MxFitFunctionR.R' 'MxRObjective.R'
'MxExpectationHiddenMarkov.R' 'MxExpectationMixture.R'
'MxExpectationStateSpace.R' 'MxExpectationBA81.R'
'MxFitFunctionGREML.R' 'MxExpectationGREML.R' 'MxMI.R'
'MxFactorScores.R' 'MxRobustSE.R' 'MxAvailableOptimizers.R'
'MxTryHard.R' 'MxSE.R' 'MxAutoStart.R' 'MxRetro.R' 'zzz.R'
Steven M. Boker [aut],
Michael C. Neale [aut],
Hermine H. Maes [aut],
Michael J. Wilde [ctb],
Michael Spiegel [aut],
Timothy R. Brick [aut],
Ryne Estabrook [aut],
Timothy C. Bates [aut],
Paras Mehta [ctb],
Timo von Oertzen [ctb],
Ross J. Gore [aut],
Michael D. Hunter [aut],
Daniel C. Hackett [ctb],
Julian Karch [ctb],
Andreas M. Brandmaier [ctb],
Joshua N. Pritikin [aut, cre],
Mahsa Zahery [aut],
Robert M. Kirkpatrick [aut],
Yang Wang [ctb],
Charles Driver [ctb],
Massachusetts Institute of Technology [cph],
S. G. Johnson [cph],
Association for Computing Machinery [cph],
Dieter Kraft [cph],
Stefan Wilhelm [cph],
Manjunath B G [cph]