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ChangeLog
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= New Features =
== 1.24 release (in progress) == #release-1.24
=== Library ===
==== Major changes ====
==== New classes ====
==== API changes ====
* SmolyakExperiment left the experimental module
* Removed deprecated method Study.printLabels
* Removed deprecated class TimerCallback
* Removed deprecated MetaModelValidation(inputSample, outputSample, metaModel) ctor
* Removed deprecated MetaModelValidation::computePredictivityFactor method
* Removed deprecated Solver.set/getMaximumFunctionEvaluation
* Removed deprecated Solver.getUsedFunctionEvaluation
* Removed deprecated Sample/CorrelationAnalysis.computePearsonCorrelation
* Removed deprecated Cobyla/TNC.setIgnoreFailure method
* Removed deprecated OptimizationAlgorithm.setMaximumEvaluationNumber method
* Removed deprecated OptimizationResult.getEvaluationNumber
=== Documentation ===
=== Python module ===
=== Miscellaneous ===
== 1.23rc1 release (2024-05-03) == #release-1.23rc1
=== Library ===
==== Major changes ====
* New GPD estimation services: MLE, profiled likelihood, time-varying, return level, covariates, clustering
* New GEV covariates estimation method, profiled likelihood by blocks
* Rank-based Sobol indices estimation
* Vector=>Field chaos-based metamodel and sensitivity algorithm
==== New classes ====
* SmoothedUniformFactory (openturns.experimental)
* GeneralizedParetoValidation (openturns.experimental)
* SamplePartition (openturns.experimental)
* PointToFieldFunctionalChaosAlgorithm (openturns.experimental)
* CubaIntegration (openturns.experimental)
* ExperimentIntegration (openturns.experimental)
* RankSobolSensitivityAlgorithm (openturns.experimental)
* UniformOrderStatistics (openturns.experimental)
==== API changes ====
* Deprecated MetaModelValidation(inputSample, outputSample, metaModel) is deprecated in favor of MetaModelValidation(outputSample, metamodelPredictions)
* Deprecated MetaModelValidation::computePredictivityFactor method, use computeR2Score
* Deprecated MetaModelValidation::getInputSample method
* Removed deprecated Pagmo.setGenerationNumber
* Removed deprecated NLopt.SetSeed
* Removed deprecated IterativeThresholdExceedance(dimension, threshold) ctor
* Removed deprecated Linear|QuadraticLeastSquares(Sample, Function) constructor
* Removed deprecated SubsetSampling.setKeepEventSample, getEventInputSample, getEventOutputSample
* Removed deprecated SubsetSampling.setISubset, setBetaMin
* Removed deprecated LHS
* Removed deprecated OptimizationAlgorithm.set/getVerbose
* Removed deprecated DickeyFullerTest.setVerbose/getVerbose
* Removed deprecated MetropolisHasting.setVerbose/getVerbose
* Removed deprecated BasisSequenceFactory.setVerbose/getVerbose
* Removed deprecated SimulationAlgorithm.setVerbose/getVerbose
* Removed deprecated WhittleFactory.setVerbose/getVerbose
* Removed deprecated CLassifier.setVerbose/getVerbose
* Removed deprecated ARMALLHF.setVerbose/getVerbose
* Removed deprecated CleaningStrategy.setVerbose/getVerbose
* Removed deprecated ApproximationAlgorithm.setVerbose/getVerbose
* Removed deprecated EllipticalDistribution.setCorrelation/getCorrelation
* Removed deprecated EllipticalDistribution.setMean
* Removed deprecated Distribution.computeDensityGenerator
* Removed deprecated Point.clean
* Removed deprecated (Inverse)Gamma.setKLambda
* Removed deprecated Os::GetEndOfLine
* Deprecated Sample|CorrelationAnalysis::computePearsonCorrelation in favor of computeLinearCorrelation
* Deprecated Cobyla::setIgnoreFailure, TNC::setIgnoreFailure in favor of setCheckStatus
* Deprecated OptimizationAlgorithm.set/getMaximumEvaluationNumber in favor of set/getMaximumCallsNumber
* Deprecated OptimizationResult.set/getEvaluationNumber in favor of set/getCallsNumber
* Deprecated Solver.set/getMaximumFunctionEvaluation in favor of set/getMaximumCallsNumber
* Deprecated Solver.getUsedFunctionEvaluation in favor of getCallsNumber
* Deprecated TimerCallback
* Deprecated ComposedDistribution in favor of JointDistribution
* Deprecated ComposedCopula in favor of BlockIndependentCopula
* Added Polygon::FillBetween static method to fill the surface between two curves
* Deprecated Study.printLabels (see getLabels)
* Removed optional parameters from Contour constructors, use set methods or ResourceMap keys to set them
=== Documentation ===
* Examples show how to visualize matrices
* Enforce check of internal links with sphinx nitpicky option
=== Python module ===
* Graphs apply default colors to Drawables with no explicit color
* Contour plots can now be filled and come with colorbars
* Binary wheels are now compatible with uv package manager
=== Miscellaneous ===
* CovarianceModel nuggetFactor can be optimized by KrigingAlgorithm
* UniformOverMesh left the experimental module
* IntegrationExpansion/LeastSquaresExpansion left the experimental module
* Allow to set optimization/simulation algorithm maximum run time duration
* New OptimizationAlgorithm API to retrieve return code and error message
* Improved TruncatedDistribution to use CDF inversion instead of rejection method to generate n-d samples
* Faster marginal distribution PDF computation by integration on marginalized components
* Extendend the JointDistribution to use any distribution defined on the unit cube that is not a copula
=== Bug fixes (total 45) ===
* #1252 (In NumericalMathFunction class, getCallsNumber and getEvaluationCallsNumber return the same information API)
* #1430 (The MetaModelValidation class has no graphics)
* #2067 (Can't compute of a marginal of a BayesDistribution->it takes ages!)
* #2218 (Split keepIntact methods)
* #2332 (The doc of Sobol' indices has issues)
* #2359 (The Sample help page does not show how to set a column)
* #2361 (Inconsistency in the documentation of KrigingAlgorithm)
* #2364 (KrigingAlgorithm: examples set bounds before disabling optimization)
* #2366 (The maths notations in the help pages are inconsistent)
* #2427 (Rename ComposedDistribution to JointDistribution ?)
* #2446 (The polynomial_sparse_least_squares theory help page can be improved)
* #2460 (Cobyla returns an internal exception when maximum number of evaluations is reached)
* #2474 (Coupling tools replace method is not robust to inputs larger than 10)
* #2477 (Deprecate Sample.computeLinearCorrelation ?)
* #2479 (Number of calls to objective function due to gradient approximation not always counted in OptimizationResult.getEvaluationNumber())
* #2489 (Inconsistent Evaluation number in drawOptimalValueHistory())
* #2500 (MarshallOlkinCopula missing computePDF ?)
* #2507 (The computeComplementaryCDF and computeSurvivalFunction methods are unclear)
* #2508 (Cobyla claim to support bounds, but does not)
* #2511 (Drop SpecFunc::IsNaN/IsInf)
* #2513 (Binomial quantile computation fails on extreme example)
* #2517 (Cannot build OpenTURNS with doc if optional dependencies bison/flex are unavailable)
* #2524 (Bayes distribution order)
* #2525 (Geometric range starts at 1)
* #2526 (BestModelChiSquared does not handle exceptions)
* #2527 (Strange probabilistic model for the fire satellite use-case)
* #2531 (StandardDistributionPolynomialFactory produces NaN and Infs)
* #2535 (undocumented SaltelliSensitivityAlgorithm model argument)
* #2541 (The computeQuantile() method of a Mixture can fail)
* #2544 (Example of multi output Kriging on the fire satellite model: paramers are not optimized)
* #2545 (Formatting Issues on LatentVariableModel)
* #2548 (Distribution::computeQuantile(p) can be computed for p<0 or p>1)
* #2552 (ComputeQuantile for discrete variables)
* #2557 (Shipping openturns and poissoninv GPL license)
* #2558 (Typos in Copulas theory documentation)
* #2563 (SimulatedAnnealingLHS.generate does not terminate for LHS of size 1)
* #2570 (MarginalDistribution test is disabled)
* #2571 (Drawables order is not preserved by the viewer)
* #2593 (Formatting of input and output arguments in the API help page)
* #2596 (The legends of the drawPDF() graph have a wrong order)
* #2602 (The pretty-print of a ParametricFunction does not show the name of the parameters)
* #2621 (TruncatedDistribution n-d CDF inversion)
* #2624 (HSICEstimatorImplementation : cannot save with pickle)
* #2627 (Weird window for NormalGamma plot in API page)
* #2628 (Multi Start have incoherent behavior if the max eval has been reached)
== 1.22 release (2024-01-09) == #release-1.22
=== Library ===
==== Major changes ====
==== New classes ====
* BoundaryMesher (openturns.experimental)
* LatentVariableModel (openturns.experimental)
* StudentCopula (openturns.experimental)
* StudentCopulaFactory (openturns.experimental)
* TruncatedOverMesh (openturns.experimental)
* SimplicialCubature (openturns.experimental)
==== API changes ====
* Removed deprecated (Non)LinearLeastSquaresCalibration::getCandidate method
* Removed deprecated (Non)GaussianLinearCalibration::getCandidate method
* Removed deprecated DomainIntersection|Union|DisjunctiveUnion(Domain, Domain) ctors
* Removed deprecated EllipticalDistribution::getInverseCorrelation method
* Removed deprecated Basis::getDimension method
* Removed deprecated HSICStat ctors relying on weight matrix
* Deprecated Pagmo.setGenerationNumber for setMaximumIterationNumber
* Deprecated NLopt.SetSeed for setSeed
* Deprecated IterativeThresholdExceedance(dimension, threshold) ctor
* Deprecated Linear|QuadraticLeastSquares(Sample, Function) constructor
* Deprecated SubsetSampling.setKeepEventSample, getEventInputSample, getEventOutputSample
* Deprecated SubsetSampling.setISubset, setBetaMin
* QuantileMatchingFactory probabilities argument is no longer optional
* Add a new moment order argument to MethodOfMomentsFactory
* Removed Drawable.getPointCode
* Deprecated LHS in favor of ProbabilitySimulationAlgorithm+LHSExperiment
* Deprecated OptimizationAlgorithm.setVerbose/getVerbose
* Deprecated DickeyFullerTest.setVerbose/getVerbose
* Deprecated MetropolisHastings.setVerbose/getVerbose
* Deprecated BasisSequenceFactory.setVerbose/getVerbose
* Deprecated SimulationAlgorithm.setVerbose/getVerbose
* Deprecated WhittleFactory.setVerbose
* Deprecated Classifier.setVerbose/getVerbose
* Deprecated ARMAlikelihoodFactory.setVerbose/getVerbose
* Deprecated CleaningStrategy.setVerbose/getVerbose
* Deprecated ApproximationAlgorithm.setVerbose/getVerbose
* Deprecated EllipticalDistribution.setCorrelation/getCorrelation
* Deprecated EllipticalDistribution.setMean
* Student.setMu/getMu now operate on Point
* Deprecated Distribution.computeDensityGenerator methods
* Deprecated Point.clean
* Deprecated (Inverse)Gamma.setKLambda
* Deprecated Os::GetEndOfLine
* Moved PosteriorDistribution to experimental
=== Documentation ===
* Improved coverage of class/methods
* New two-degree-of-freedom oscillator use-case
=== Python module ===
* New Graph.setLegendCorner method to set legend outside of Graph
* Allowing use of matplotlib markers and legend location strings in Graphs
=== Miscellaneous ===
* Improved pretty-printing of chaos functions, distributions
* Added IterativeThresholdExceedance::getRatio
* Added FunctionalChaosResult::drawSelectionHistory to plot LARS coefs paths
* New API in SubsetSampling to get samples at each iteration
* Added SubsetSampling method to set the initial experiment
* Allowing use of non-independent copulas in LHSExperiment
* Improved system events to allow more kinds of events like DomainEvent
* Add CMake presets file
=== Bug fixes ===
* #1338 (The example in UserDefined does not show how to create a uniform discrete distribution)
* #1670 (IntervalMesher segfault when diamond==true)
* #1896 (Operator == inconsistencies)
* #1979 (The doc of KernelSmoothing has issues)
* #2195 (SubsetSampling: keep the failed and secure points at each step)
* #2216 (Characteristic function of the Triangular and Trapezoidal distributions)
* #2220 (Negative value given by the computeComplementaryCDF method)
* #2235 (Documentation is missing for some methods of class Matrix)
* #2339 (FunctionalChaosSobolIndices has doc issues)
* #2347 (Build error of /usr/include/tbb/machine/gcc_generic.h:39:20: error: operator '||' has no left operand in s390x on Fedora)
* #2351 (Import error in the draw method example)
* #2354 (IterativeThresholdExceedance)
* #2371 (t-Copula implementation)
* #2403 (KrigingAlgorithm fails if basis is empty)
* #2406 (Student does not give access to all its parameters)
* #2412 (The class QuadraticLeastSquares returns a wrong quadratic term)
* #2420 (Deprecate Os::GetEndOfLine())
* #2423 (mutable OptimizationAlgorithm)
* #2429 (Elliptical distributions)
* #2431 (LHSExperiment.generate() can fail)
* #2434 (WeibullMaxMuSigma: default values are not good)
* #2437 (Binomial computeSurvivalFunction error)
* #2439 (Use more computeScalarQuantile)
* #2442 (EGO does not handle maximization problems)
* #2443 (C++ Test SmolyakExperiment_std fails on arm64, ppc64el, s390x)
* #2452 (The LHSExperiment does not manage a non-independent copula)
* #2456 (configure fails to find bonmin)
* #2459 (Problems while configuring OpenTURNS for Visual Studio 2019)
* #2463 (Normal.computeConditionalPDF() is wrong when the components are independent)
* #2466 (PlackettCopula covariance matrix)
* #2481 (Improve SpectralGaussianProcess sampling speed)
* #2486 (DrawParallelCoordinates is not correct)
* #2487 (Cannot save large datasets using XMLH5StorageManager)
* #2503 (Using KernelSmoothing can make Jupyter Notebook to hang)
== 1.21 release (2023-06-20) == #release-1.21
=== Library ===
==== Major changes ====
* New GEV estimation services: MLE, profiled likelihood, r-maxima, time-varying, return level
==== New classes ====
* SmolyakExperiment (openturns.experimental)
* Physical|StandardSpaceCrossEntropyImportanceSampling, CrossEntropyResult (openturns.experimental)
* LeastSquaresExpansion, IntegrationExpansion (openturns.experimental)
* UniformOverMesh (openturns.experimental)
* GeneralizedExtremeValueValidation (openturns.experimental)
* Coles (openturns.usecases.coles)
* Linthurst (openturns.usescases.linthurst)
==== API changes ====
* Deprecated LinearLeastSquaresCalibration::getCandidate, use getStartingPoint
* Deprecated NonLinearLeastSquaresCalibration::getCandidate, use getStartingPoint
* Deprecated GaussianLinearCalibration::getCandidate, use getParameterMean
* Deprecated NonGaussianLinearCalibration::getCandidate, use getParameterMean
* Removed SequentialStrategy
* Removed TensorApproximationAlgorithm, TensorApproximationResult, CanonicalTensorEvaluation, CanonicalTensorGradient
* Removed FunctionalChaosSobolIndices.getSobolGrouped(Total)Index(int)
* Removed FunctionalChaosSobolIndices::summary
* Removed CorrelationAnalysis::PearsonCorrelation|SpearmanCorrelation|PCC|PRCC
* Removed Distribution.getStandardMoment
* Removed deprecated LinearModelAlgorithm | LinearModelStepwiseAlgorithm ctors
* Removed FunctionalChaosAlgorithm(Function) ctors
* Removed MetaModelResult(Function, Function, ...) ctor
* Removed FunctionalChaosResult.getComposedModel, MetaModelResult.getModel and associated attributes for 1.21
* Removed FunctionalChaosResult Function ctor
* Removed (Process)Sample::computeCenteredMoment
* Removed Distribution::getCenteredMoment
* Removed IterativeMoments::getCenteredMoments
* Removed GeneralLinearModelAlgorithm | KrigingAlgorithm ctors
* Removed Drawable.draw|clean & Graph.draw|clean|getRCommand|makeR*|GetExtensionMap methods
* Removed Sample.storeToTemporaryFile|streamToRFormat methods
* Removed GaussianProcess.GIBBS
* Deprecated DomainIntersection|Union|DisjunctiveUnion(Domain, Domain) ctors
* KrigingResult.getTrendCoefficients now returns a single Point
* GeneralLinearModelResult.getTrendCoefficients now returns a single Point
* KrigingResult.getBasisCollection was removed in favor of KrigingResult.getBasis which returns a single Basis
* GeneralLinearModelResult.getBasisCollection was removed in favor of GeneralLinearModelResult.getBasis which returns a single Basis
* Deprecated EllipticalDistribution::getInverseCorrelation
* HSICStat arguments updated (covariance matrices instead of data + covariance models)
* Deprecated HSICStat relying on weight matrix
* Deprecated Basis::getDimension, use getInputDimension instead
* Removed thinning from Gibbs and all MetropolisHastings classes
* Removed burn-in from Gibbs and all MetropolisHastings classes except RandomWalkMetropolisHastings
* RandomWalkMetropolisHastings::getRealization|Sample no longer remove states reached during burn-in
* RandomWalkMetropolisHastings default adaptation parameters were changed to enable adaptation
* Split BoxCoxFactory::build into buildWithGraph, buildWithLM, buildWithGLM
=== Documentation ===
* Add API documentation to common use cases pages
* Added new use case: Linthurst
=== Python module ===
=== Miscellaneous ===
* Add HypothesisTest::LikelihoodRatioTest for nested model selection
* Add VisualTest::DrawPPplot
* Add VisualTest.Draw(Upper|Lower)(Tail|Extremal)DependenceFunction methods to plot dependence functions
* Add Distribution.compute(Upper|Lower)(Tail|Extremal)DependenceMatrix methods to compute dependence coefficients
* Enable Pagmo.moead_gen with pagmo>=2.19
* Enable Bonmin.Ecp/iFP algorithms with bonmin>=1.8.9
* BoxCoxFactory handles linear model
=== Bug fixes ===
* #2045 ([Debian] NLopt issues during tests on arm64, ppc64el, s390x)
* #2046 ([Debian] DistFunc_binomial issues in tests on arm64 and ppc64el)
* #2047 ([Debian] pythoninstallcheck_DistributionFactory_std issues on arm64, armel, armhf, mips64el)
* #2153 (HSIC computation cost)
* #2185 (Error: no member named '__1' in namespace 'std' ARM64 Android Termux)
* #2193 (Tiny spelling issues)
* #2194 (SubsetSampling: bug in 1.15)
* #2200 (out of bound probas)
* #2204 (Build fails with primesieve-11.0)
* #2205 (Edges of a PolygonArray)
* #2206 (Only Contour legends are shown in a graph mixing Contour and other Drawable (Curve, Cloud,...))
* #2209 (The Felhberg algorithm can fail, sometimes)
* #2210 (Python Domain.getImplementation does not work)
* #2213 (Some script fails)
* #2229 (Unary minus for distribution)
* #2240 (The Viewer sometimes fail)
* #2250 (HSIC Target sensitivity bad filtering)
* #2252 (The examples of calibration are difficult to understand)
* #2255 (The description of a Distribution is wrong)
* #2268 (MeixnerDistribution: slow pdfgradient)
* #2274 (Basis accepts functions with different input (output) dimensions)
* #2281 (The marginal of FireSatelliteModel can fail)
* #2285 (The input and output descriptions does not go down to the metamodel)
* #2287 (DesignProxy.computeDesign() can produce a segmentation fault)
* #2296 (DiracCovarianceModel.discretize() is buggy)
* #2297 (Legend location with grid layout)
* #2299 (Allow openturns as subproject)
* #2306 (Dirichlet::computeConditionalPDF can produce NaNs and Infs)
* #2323 (Minor wording issues in the cross entropy importance sampling example)
* #2327 (A sign mistake in FGM document)
* #2328 (Sample.add weird behaviour)
* #2338 (HSIC with large amount of data makes crash)
== 1.20 release (2022-11-08) == #release-1.20
=== Library ===
==== New classes ====
* IndependentCopulaFactory
* FieldToPointFunctionalChaosAlgorithm (openturns.experimental)
* FieldFunctionalChaosResult (openturns.experimental)
* FieldFunctionalChaosSobolIndices (openturns.experimental)
* CorrelationAnalysis
* UserDefinedMetropolisHastings (openturns.experimental)
* QuantileMatchingFactory
* UniformMuSigma
==== API changes ====
* Removed coupling_tools.execute get_stderr,get_stdout arguments
* Removed deprecated Nlopt|Ceres|Bonmin|CMinpack|Ipopt|TBB|HMatrixFactory::IsAvailable methods
* Deprecated SequentialStrategy
* Deprecated TensorApproximationAlgorithm, TensorApproximationResult, CanonicalTensorEvaluation, CanonicalTensorGradient
* Deprecated FunctionalChaosSobolIndices.getSobolGrouped(Total)Index(int)
* Deprecated static CorrelationAnalysis::PearsonCorrelation|SpearmanCorrelation|PCC|PRCC, use the methods of the new CorrelationAnalysis class instead.
* Removed static CorrelationAnalysis::SRC|SRRC due to a bug: #1753.
* Deprecated Distribution::getStandardMoment
* Inverted ctors arguments order in LinearModelAlgorithm & LinearModelStepwiseAlgorithm : first sample, then basis
* Deprecated oldest LinearModelAlgorithm(X, basis, Y) and LinearModelStepwiseAlgorithm(X, basis, Y,...) ctors
* Deprecated FunctionalChaos(Function, ...) ctors
* Deprecated MetaModelResult(Function, Function, ...) ctor
* Deprecated FunctionalChaosResult.getComposedModel, MetaModelResult.getModel
* Deprecated FunctionalChaosResult(Function, ...) ctor
* Deprecated (Process)Sample::computeCenteredMoment in favor of computeCentralMoment
* Deprecated Distribution::getCenteredMoment in favor of getCentralMoment
* Deprecated IterativeMoments::getCenteredMoments in favor of getCentralMoments
* Deprecated GeneralLinearModelAlgorithm | KrigingAlgorithm ctors using collection of basis
* Deprecated Drawable.draw|clean & Graph.draw|clean|getRCommand methods for legacy R graphs
* Deprecated SampleImplementation.storeToTemporaryFile|streamToRFormat
* Deprecated GaussianProcess.GIBBS in favor of GaussianProcess.GALLIGAOGIBBS
* Deprecated FunctionalChaosSobolIndices::summary in favor of __str__
* Deprecated BoxCoxFactory::build method using collection of basis
=== Documentation ===
* Added example galleries at the end of API doc pages
* Added new use case: WingWeightModel and example of use
* Added new use case: FireSatelliteModel and example of use
=== Python module ===
* New openturns.experimental submodule introducing newest classes until stabilization
=== Miscellaneous ===
* Chaos for mixed variables
=== Bug fixes ===
* #1214 (There is no example with IntegrationStrategy on a database)
* #1333 (Polynomial chaos with mixed variables (improvement))
* #1473 (FunctionalChaosResult should provide the used input/output samples )
* #1568 (There is no example to bootstrap the polynomial chaos)
* #2030 (There is no example which shows how to calibrate a model without observed inputs)
* #2043 (Some attributes of PythonDistribution are changed during the lifecycle of the object)
* #2058 (TruncatedNormal failure)
* #2059 (Pb with computeConditionalPDF in KernelMixture)
* #2064 (drop getGroupedSobolIndex(int) ?)
* #2073 (getCenteredMoment(0))
* #2076 (Problem in TruncatedDistribution of discrete distributions)
* #2083 (Problem in the QQplot of a discrete distribution)
* #2088 (Why is this library overriding the SIGINT handler?)
* #2089 (Cannot load Python objects with large attributes in a Study)
* #2097 (LinearModelStepwiseAlgorithm ctor order)
* #2091 (Compressed H5 files?)
* #2094 (getSampleAtVertex() is not robust)
* #2095 (Triangular::computeCharacteristicfunction() produces NaNs)
* #2098 (LinearModelStepwiseAlgorithm null basis)
* #2101 (drop FunctionalChaosSobolIndices::summary)
* #2103 (FunctionalChaosRandomVector notes)
* #2110 (HSIC draw indices method does not handle input sample description names)
* #2115 (Add mini-galleries of examples on the API pages)
* #2121 (GaussianProcess Gibbs sampling method issues)
* #2123 (MixtureClassifier 'grade' method does not work with a Sample input)
* #2125 (SciPyDistribution __init__ failure with scipy 1.9.0)
* #2129 (get samples from Wishart distribution)
* #2139 (KarhunenLoeveSVDAlgorithm seems to truncate the expansion from v1.19)
* #2140 (GeneralizedParetoFactory.buildMethodOfMoments estimating wrong parameter)
* #2145 (Calibration wo obs input API)
* #2152 (P1LagrangeInterpolation can make Python fail)
* #2154 (SpaceFillingMinDist: LaTeX typo)
* #2157 (Slow creation of mixtures when the atom distributions are costly to copy)
* #2161 (Drop Normal SPD check)
* #2176 (Missing documentation on Kriging Result methods)
== 1.19 release (2022-05-10) == #release-1.19
=== Library ===
==== Major changes ====
* HSIC sensitivity indices
* RandomWalkMetropolisHastings now updates all components at a time, previously updated componentwise
* Iterative statistics
==== New classes ====
* RandomVectorMetropolisHastings
* IndependentMetropolisHastings
* Gibbs
* HSICEstimatorConditionalSensitivity
* HSICEstimatorGlobalSensitivity
* HSICEstimatorTargetSensitivity
* HSICUStat
* HSICVStat
* IterativeExtrema
* IterativeMoments
* IterativeThresholdExceedance
* TensorProductExperiment
* NAIS
* Pagmo
* GalambosCopula
==== API changes ====
* Removed deprecated Hanning alias
* Removed deprecated AdaptiveDirectionalSampling alias
* Removed deprecated VisualTest::DrawCobWeb method
* Removed deprecated shims module
* Removed deprecated TBB.SetNumberOfThreads/GetNumberOfThreads
* Removed deprecated MultiFORM.setMaximumNumberOfDesignPoints/getMaximumNumberOfDesignPoints
* Removed deprecated SubsetSampling.getNumberOfSteps
* coupling_tools.execute: deprecated get_stderr,get_stdout for capture_output, returns CompletedProcess
* Deprecated Nlopt|Ceres|Bonmin|CMinpack|Ipopt|TBB|HMatrixFactory::IsAvailable in favor of PlatformInfo::HasFeature, see also GetFeatures
* RandomWalkMetropolisHastings API breaks to split the likelihood definition
* Deprecated CalibrationStrategy class
=== Documentation ===
=== Python module ===
* Hide C++ getImplementation methods and add Python-specific getImplementation methods with automatic dynamic typecasting
* Add Sample.asDataFrame/BuildFromDataFrame pandas conversion methods
=== Miscellaneous ===
* Add Collection::select(Indices) method
* Parallelized SymbolicFunction evaluation (enabled from a sample size>100 with exprtk)
* Sample csv export: allow one to set precision and format
* Sample csv import: handle nan/inf values
* Add EnumerateFunction::getBasisSizeFromTotalDegree(maximumDegree)
=== Bug fixes ===
* #1260 (RandomWalkMetropolisHastings does not handle non-symmetric proposals properly)
* #1263 (Static functions are implemented in C++ classes instead of namespaces)
* #1334 (Multi-objective optimization)
* #1444 (The calibration examples are unclear)
* #1830 (Incompatible documentation for CauchyModel)
* #1914 (Cobyla & NLopt should accept LeastSquaresProblem)
* #1921 (Adaptive Directional Stratification: the coefficient of variation of the probability is inconsistent with repeated probability results)
* #1922 (Adaptive Directional Stratification: the coefficient of variation cannot be used as a stopping criterion)
* #1926 (Examples have broken graphics)
* #1931 (Interface class getImplementation methods are useless in Python)
* #1939 (Contour curves outside graph window)
* #1940 (getMaximumDegreeStrataIndex has a bug with hyperbolic rule)
* #1943 (TriangularMatrix * Point --> ScalarCollection)
* #1946 (Doc search issues)
* #1947 (The constructor of FunctionalChaosResult is undocumented)
* #1949 (The comparison operator of ComposedDistribution is wrong)
* #1958 (Performance of OrthogonalUniVariatePolynomial (precision, speed))
* #1959 (Cobyla run causes segfault when empty ctor is used)
* #1993 (IsotropicCovarianceModel has no default ctor)
* #1994 (default GaussProductExperiment is invalid)
* #1997 (KLSVD spectrum cutoff error)
* #2012 (Cannot sample a ConditionedGaussianProcess on a mesh containing conditioning points)
* #2014 (Precision issue with the Python KrigingAlgorithm test)
* #2019 (Parametric stationary covariance models doc section needs refresh)
* #2020 (Problem with the UserDefined distribution)
* #2025 (The lack of Bonmin generates an error during example compilation)
* #2031 (Multi-objective optimization example question)
* #2032 (Documentation of NAIS)
== 1.18 release (2021-11-10) == #release-1.18
=== Library ===
==== Major changes ====
==== New classes ====
* DistanceToDomainFunction
==== API changes ====
* Removed deprecated MultiStart::setStartingPoints/getStartingPoints
* Removed deprecated KarhunenLoeveResult::getEigenValues
* Removed deprecated coupling_tools.execute is_shell/workdir/shell_exe/check_exit_code arguments
* RandomVector::get/setParameter and getParameterDescription available to PythonRandomVector
* Implemented CovarianceModel::computeCrossCovariance
* Deprecated Hanning in favor of Hann
* Deprecated AdaptiveDirectionalSampling in favor of AdaptiveDirectionalStratification
* Deprecated VisualTest::DrawCobWeb in favor of DrawParallelCoordinates
* IndicatorFunction constructor takes a Domain as input
* Intervals and DomainUnions get new method computeDistanceToDomain(Point or Sample)
* Renamed some ResourceMap keys: cache-max-size>Cache-MaxSize, parallel-threads>TBB-ThreadsNumber
* Deprecated TBB.SetNumberOfThreads/GetNumberOfThreads
* Deprecated MultiFORM.setMaximumNumberOfDesignPoints/getMaximumNumberOfDesignPoints
* Deprecated SubsetSampling.getNumberOfSteps
* Normal and Student distributions: no need to specify the R parameter if it is the identity matrix
* KrigingResult::getConditionalMean and getConditionalMarginalVariance yield Samples instead of Points when applied to Samples
* Deprecated shims module
=== Documentation ===
=== Python module ===
=== Miscellaneous ===
=== Bug fixes ===
* #1840 (HMatrixFactory leaks)
* #1842 (libOT.so.0.0.0 doesn't have a SONAME)
* #1843 (Ali-Mikhail-Haq copula parameter value)
* #1844 (MaximumDistribution::computePDF is wrong when all the marginals are equal and independent)
* #1845 (MemoizeFunction does not propagate to finite difference gradient&hessian)
* #1847 (ParametricFunction require unnecessary function evaluations)
* #1854 (Sample indexing does not work on np.int64 type)
* #1856 (Speed up Normal computeSurvivalFunction)
* #1857 (ProductCovarianceModel fails when constructed with DiracCovarianceModel)
* #1858 (Normal::computeCDF(sample) crash for large dimensions)
* #1861 (Still some instabilities in Kriging with ot.StationaryFunctionalCovarianceModel)
* #1864 (FORM - IMPORTANCE FACTOR)
* #1868 (Not compatible with hmat-oss-1.7.1: no member named 'compressionMethod' in 'hmat_settings_t')
* #1870 (TruncatedDistribution fails to compute quantiles)
* #1874 (ProcessSample.getSampleAtVertex() may be useful as a public method)
* #1877 (How to model singular multivariate distributions?)
* #1878 (Rename Hanning filtering window)
* #1879 (Adaptive Directional Sampling Algorithm: the drawProbabilityConvergence graph may be wrong)
* #1880 (Adaptive Directional Sampling: some enhancements proposed)
* #1882 (Is Distribution::getLinearCorrelation useful ?)
* #1883 (Strange behavior of FORM ?)
* #1884 (setDesign can lead to wrong Sobol' indices)
* #1891 (Correlation of Halton sequence at high dimensions)
* #1911 (DirectionalSampling freezes python if not correctly initialized)
* #1912 (splitter: missing doc)
* #1915 (computePDFGradient(const Sample &) should rely on computePDFGradient(const Point &) in DistributionImplementation)
* #1918 (PythonDistribution does not allow one to overload the isDiscrete() method)
== 1.17 release (2021-05-12) == #release-1.17
=== Library ===
==== Major changes ====
==== New classes ====
* VertexValuePointToFieldFunction
* KarhunenLoeveReduction
* KarhunenLoeveValidation
* IsotropicCovarianceModel
* KroneckerCovarianceModel
* VonMisesFactory
* KFoldSplitter, LeaveOneOutSplitter
==== API changes ====
* Removed deprecated Pairs alias
* Removed deprecated SORMResult::getEventProbabilityHohenBichler/getGeneralisedReliabilityIndexHohenBichler
* Removed deprecated OptimizationResult::getLagrangeMultipliers, OptimizationAlgorithm::computeLagrangeMultipliers
* Removed deprecated FittingTest::Kolmogorov, FittingTest::BestModelKolmogorov (DistributionFactory argument only)
* Deprecated Sample::computeStandardDeviationPerComponent, use computeStandardDeviation
* Deprecated KarhunenLoeveResult::getEigenValues, use getEigenvalues
* Removed StationaryCovarianceModel
* CovarianceModel.computeAsScalar allows scalars
* Swapped SimulatedAnnealingLHS constructor SpaceFilling/TemperatureProfile arguments
* Added EfficientGlobalOptimization::getKrigingResult, gets the updated version of the KrigingResult passed to the constructor
* Deprecated MultiStart::setStartingPoints/getStartingPoints, use MultiStart::setStartingSample/getStartingSample instead
* MultiStart::setStartingPoint/getStartingPoint throws (previously did nothing)
=== Documentation ===
* Fixed example and plot of Kolmogorov statistics.
* Added a new example showing how to combine RandomWalkMetropolisHastings and PythonDistribution
=== Python module ===
* Serialize Python wrapper objects using dill (PythonDistribution, PythonFunction ...)
=== Miscellaneous ===
=== Bug fixes ===
* #1010 (The doc for ExpectationSimulationAlgorithm is confusing)
* #1052 (The Dirichlet and Normal distributions only have 1D graphics in the doc)
* #1224 (The examples in the doc of the DomainEvent class are unclear)
* #1229 (Better encapsulation of optional 3rd-party headers)
* #1240 (There is no theory documentation for ExpectationSimulationAlgorithm)
* #1257 (Cannot create a SimulatedAnnealingLHS without specifying the temperature profile)
* #1287 (The constructors of KernelSmoothing have no doc)
* #1418 (The Contour example does not present the second constructor)
* #1425 (There is no method to create a train / test pair)
* #1431 (The figure for LHSExperiment is not accurate)
* #1459 (There is no example which shows how to set a column of a Sample)
* #1497 (There is no example to create a multivariate Normal distribution)
* #1506 (The Brent class has no example)
* #1570 (Wrong formula for MauntzKucherenkoSensitivityAlgorithm)
* #1650 (There is no example of a parametric StationaryFunctionalCovarianceModel)
* #1656 (The help page of ExpectationSimulationAlgorithm is unclear)
* #1661 (XMLH5StorageManager does not store IndicesCollection into HDF5 files)
* #1662 (Operator() of a CovarianceModel with multidimensional output should yield object of type SquareMatrix)
* #1680 (QuadraticBasisFactory multiplies cross products by 2)
* #1710 (Misleading y labels in VisualTest.DrawPairsMarginals())
* #1713 (The doc of the ResourceMap does not match the content of the ResourceMap)
* #1714 (The log-PDF of the Pareto is wrong)
* #1721 (The `draw` method of `SobolSimulationAlgorithm` does not reuse the descriptions)
* #1723 (There is no interesting example of the ExprTk feature of SymbolicFunction)
* #1725 (The BetaFactory help page has wrong equations)
* #1729 (LinearModelAnalysis sometimes fail)
* #1731 (The Extreme value example may be improved)
* #1737 (Low rank tensor doc issues)
* #1742 (The example which shows how to set the figure size is wrong.)
* #1751 (DrawCorrelationCoefficients has a wrong Text height)
* #1752 (Error while estimating the reduced log-likelihood when using a StationaryFunctionalCovarianceModel)
* #1758 (The LinearModelStepwiseAlgorithm has no example)
* #1759 (Kriging model with StationaryFunctionalCovarianceModel might provide bad results)
* #1768 (Bug in ExponentiallyDampedCosineModel & SphericalModel)
* #1771 (GridLayout hides the titles in the graph)
* #1772 (MinimumVolumeClassifier cannot draw 1D samples)
* #1774 (ExponentialModel::partialGradient is wrong)
* #1775 (ExponentialModel does not account correlation with Covariance ctor)
* #1776 (Typo in the Branin use case implementation)
* #1781 (The link_to_an_external_code example has small bugs)
* #1784 (The drawPDF method of Histogram sometimes fail)
* #1787 (CovarianceMatrix from SymmetricMatrix raises InvalidArgumentException)
* #1790 (LinearModelResult.getFormula() method is not updated by Stepwise regression)
* #1794 (Some doc examples seem to behave with new infrastructure)
* #1796 (VonMisesFactory is missing)
* #1803 (The API example of Experiment has a format issue)
* #1805 (Brent's implementation has a stability issue)
* #1807 (Beta::computeCharacteristicFunction is wrong if the lower bound is not zero)
* #1815 (1.16 fails with dlib-cpp-19.22)
* #1818 (Problem with wilksNumber)
* #1820 (Incoherent results in LinearModelAnalysis)
* #1835 (RegularizedIncompleteBeta returns nan)
* #1836 (Hypergeometric results differ without boost)
* #1837 (Poor performance when using pickle on OT objects)
== 1.16 release (2020-11-16) == #release-1.16
=== Library ===
==== Major changes ====
* Drop normalization in KrigingAlgorithm
* Drop normalization & transformation handling in GeneralLinearModelAlgorithm
* XML/H5 storage (hdf5 library)
* C++11 requirement
==== New classes ====
* BlockIndependentDistribution
* FejerAlgorithm
* GridLayout
* MinimumVolumeClassifier
* StationaryFunctionalCovarianceModel
* XMLH5StorageManager
==== API changes ====
* Removed deprecated Weibull, WeibullFactory, WeibullMuSigma classes
* Removed deprecated GumbelAB class
* Removed deprecated Event class
* Removed deprecated EnumerateFunction constructors
* Removed various deprecated distribution accessors
* Deprecated Pairs class, see VisualTest.DrawPairs
* Deprecated SORMResult::getEventProbabilityHohenBichler, use SORMResult::getEventProbabilityHohenbichler instead
* Deprecated SORMResult::getGeneralisedReliabilityIndexHohenBichler, use SORMResult::getGeneralisedReliabilityIndexHohenbichler instead
* Renamed SobolSequence::MaximumNumberOfDimension as SobolSequence::MaximumDimension
* Added VisualTest::DrawLinearModel(linearModelResult), useful if the test is performed on the training samples
* Added VisualTest::DrawLinearModelResidual(linearModelResult), useful if the test is performed on the training samples
* Deprecated OptimizationResult::getLagrangeMultipliers
* Moved OptimizationAlgorithm::computeLagrangeMultipliers to OptimizationResult
* Added AIC & BestModelAIC static methods in FittingTest
* Added AICC & BestModelAICC static methods in FittingTest
* Moved BuildDistribution from FunctionalChaosAlgorithm to MetaModelAlgorithm
* Added Drawable::BuildRainbowPalette(size)
* Added Drawable::BuildTableauPalette(size), which is now the default palette.
* Added Drawable::ConvertFromRGBIntoHSV
* Added FittingTest::Lilliefors, BestModelLilliefors
* Deprecated FittingTest::BestModelKolmogorov(Sample, DistributionFactoryCollection, TestResult), use BestModelLilliefors
* Deprecated FittingTest::Kolmogorov(Sample, DistributionFactory, TestResult, level), use Lilliefors
* MetamodelValidation: now computePredictivityFactor returns Point, drawValidation return GridLayout
* Deprecated coupling_tools.execute is_shell/workdir/shell_exe/check_exit_code arguments
=== Documentation ===
* Sphinx-gallery used to render examples
==== API documentation ====
* Clarified SobolIndicesExperiment page, notations now consistent with SobolIndicesAlgorithm page
* Clarified SobolIndicesAlgorithm and (Saltellli|Martinez|MauntzKucherenko|Jansen)SensitivityAlgorithm pages, corrected formulas
* Documented how to turn warnings off or write them on a file
=== Python module ===
* Renamed Viewer *_kwargs arguments to *_kw (matplotlib convention)
* Add ProcessSample Field accessors
=== Miscellaneous ===
* Do not compute Lagrange multipliers by default during an optimization
* Add ResourceMap::FindKeys
* Allow computeLogPDF methods to output values lower than SpecFunc::LogMinScalar
=== Bug fixes ===
* #1001 (Add method SobolSimulationResult::draw)
* #1259 (The diagonal of a scatter plot matrix should have the histograms)
* #1267 (Some CSV files cannot be imported)
* #1377 (The `setKnownParameter` method is not compatible with `buildEstimator`)
* #1407 (GeneralLinearModelAlgorithm mishandles user-specified scale parameter when normalize is True)
* #1415 (The BuildDistribution static method should not use the KS-test)
* #1421 (UserDefinedStationaryCovarianceModel doc suggests input dimension can be >1)
* #1436 (The style of the curves is unpleasing to my eyes)
* #1447 (Highly inaccurate result in reliability model when using subset of RandomVector)
* #1465 (The Sample constructor based on a list and an integer should not exist)
* #1470 (setNbModes is sometimes ignored)
* #1474 (optimization defaults)
* #1507 (Leak in Collection typemaps)
* #1510 (ot.Ceres('LEVENBERG_MARQUARDT') and ot.Ceres('DOGLEG') do not handle bound constraints)
* #1515 (KernelSmoothing build failure)
* #1520 (The NLopt test is dubious)
* #1521 (Basis of MonomialFunction)
* #1529 (The error of the NonLinearLeastSquaresCalibration and GaussianNonLinearCalibration are different)
* #1540 (SubsetSampling: incorrect event sample)
* #1547 (Mesh does not check the simplices indices)
* #1549 (Doc of evaluation operator of KrigingResult)
* #1553 (Optimization algorithms ignore MaxEvaluationNumber parameter in SORM)
* #1556 (WeibullMin::computePDFGradient yields the partial derivatives in the wrong order)
* #1558 (Example estimate_multivariate_normal: FittingTest::BestModelBIC fails to compute the BIC)
* #1564 (Set a Point makes OT crash)
* #1567 (The API doc of SobolIndicesExperiment has a format issue)
* #1573 (LinearModelAnalysis::drawQQPlot line is not the first bisector)
* #1578 (Option to suppressing and/or save warnings?)
* #1581 (LinearModelAlgorithm run() fails to parse Sample description)
* #1586 (Documentation: description error in the API for the FittingTest_BestModelKolmogorov and FittingTest_BestModelChiSquaredclasses)
* #1590 (The equation of the Fejer quadrature rule is triplicated)
* #1592 (SubsetSampling returns an error if Pf=1)
* #1594 (LinearLeastSquaresCalibration and CalibrationResult)
* #1599 (FieldToPointConnection-BlockSize is missing)
* #1603 (FieldToPointConnection generates an invalid exception)
* #1605 (MaximumLikelihoodFactory cannot be used with FittingTest.Kolmogorov)
* #1624 (The graphs_loglikelihood_contour example has a bug)
* #1642 (Big white space at the beginning of examples)
* #1643 (Problem in MaximumDistribution PDF)
* #1647 (MCMC::computeLogLikelihood does not compute the log-likelihood)
* #1651 (Cobyla freezes in 0T1.16rc1)
* #1658 (TimeSeries accessor)
* #1660 (Cannot extract continuous modes from KLResult when dimension>1)
* #1668 (LevelSetMesher does not take into account the comparison operator)
== 1.15 release (2020-05-25) == #release-1.15
=== Library ===
==== Major changes ====
* New EV solver for KarhunenLoeveP1Algorithm (Spectra), with sparse matrix and HMatrix support
* Enable HMat AcaRandom compression method
==== New classes ====
* Ipopt optimization solver
==== Documentation ====
==== API changes ====
* Removed deprecated OptimizationAlgorithm::GetLeastSquaresAlgorithmNames
* Removed deprecated GaussianNonLinearCalibration,NonLinearLeastSquaresCalibration::set,getAlgorithm
* Removed deprecated MethodOfMomentsFactory::set,getOptimizationProblem
* ResourceMap::Set* methods no longer add new keys, the new Add* methods must be used instead
* Removed OPTpp
* Renamed HistogramFactory::computeSilvermanBandwidth into HistogramFactory::computeBandwidth.
=== Python module ===
* ProcessSample __getitem__ returns Sample instead of Field
* Implement list indexing
=== Miscellaneous ===
* Add Sample::getMarginal(Description)
* Fixed TBB performance when used together with OpenBLAS
=== Bug fixes ===
* #1124 (DistributionFactory::buildAsXXX methods not documented)
* #1213 (The legend of the graphics in MetaModelValidation is wrong)
* #1222 (There is no kriging example based on HMAT)
* #1331 (FittingTest_BestModelBIC sometimes fail)
* #1335 (The return of the unsafe ResourceMap)
* #1337 (The rDiscrete function has no help page)
* #1349 (Problem in the graph of Histogram)
* #1351 (Text has a zero size)
* #1354 (The doc of GeneralizedParetoFactory does not reflect the implementation)
* #1371 (Memory leak in ot.TruncatedDistribution)
* #1372 (wrap MultiStart::OptimizationResultCollection)
* #1374 (The setKnownParameter of the factories are not documented enough)
* #1376 (The View class has no example)
* #1378 (Kriging-related covariance model weirdness)
* #1383 (The ExprTk engine for SymbolicFunction does not document the "var" keyword)
* #1384 (The ExprTk engine is not case-sensitive)
* #1388 (Kolmogorov fails on a PythonDistribution)
* #1390 (The doc for the `computeQuantile` method does not describe the optional `tail` argument)
* #1393 (SobolSimulationAlgorithm should be simpler)
* #1395 (Indexing Sample improvement)
* #1403 (Python import otagrum throws an error)
* #1404 (Bug in KrigingAlgorithm+hmat-oss)
* #1405 (Most of the simulation algorithms for rare event fail on a coronavirus example)
* #1416 (MethodOfMomentsFactory has no setOptimizationBounds method)
* #1419 (BoundingVolumeHierarchy segaults/hangs)
* #1423 (The computeSilvermanBandwidth of the HistogramFactory has no help)
* #1432 (Expected improvement-based EfficientGlobalOptimization stopping criterion could be improved)
* #1437 (OrderStatisticsMarginalChecker bound message)
* #1438 (Normal distribution: computeComplementaryCDF)
* #1443 (Not all distributions have a getRoughness() method)
* #1448 (Memory consumption leads to crash)
* #1449 (P1LagrangeInterpolation sometimes fails)
* #1455 (GLM::setCovarianceModel could lead to unexpected behavior of parameter optimization in KrigingAlgorithm)
* #1456 (truncation of distribution)
* #1461 (ComparisonOperator().getImplementation().getClassName() segfault)
* #1471 (The graphics of KarhunenLoeveQuadratureAlgorithm has no axes)
* #1485 (The Normal().getRoughness() method is wrong)
* #1495 (Wrong formula for Expected Improvement evaluation)
== 1.14 release (2019-11-13) == #release-1.14
=== Library ===
==== IMPORTANT: Distributions parametrization changes ====
* New argument ordering in Frechet ctor: scale(beta), shape(alpha), location(gamma) (swapped alpha and beta)
* New parametrization in Gumbel: scale(beta), position(gamma) (beta=1/alpha for first argument)
* New parametrization in Beta: shape(alpha), shape(beta), location(a), location(b) (beta=t-r for second argument)
* New parametrization in InverseGamma: rate(lambda), shape(k) (swapped arguments)
* New parametrization in InverseNormal: location(mu), rate(lambda) (swapped arguments)
* New parametrization in Laplace: mean(mu), rate(lambda) (swapped arguments)
=> One can use "import openturns.shims as ot" to maintain compatibility with older scripts
==== Major changes ====
* New optimization solvers (Dlib, Bonmin for mixed integer optimization problems)
* New distributions (SquaredNormal, WeibullMax, Pareto, DiscreteCompoundDistribution, MixedHistogramUserDefined)
* New estimators (ParetoFactory, GeneralizedExtremeValueFactory, LeastSquaresDistributionFactory, PlackettCopulaFactory)
* New copulas (JoeCopula, MarshallOlkinCopula, PlackettCopula)
* Linear model learner (LinearModelStepwiseAlgorithm)
* System events (IntersectionEvent, UnionEvent, SystemFORM, MultiFORM)
==== New classes ====
* Dlib
* SquaredNormal
* NullHessian
* GumbelLambdaGamma
* LogNormalMuErrorFactor
* WeibullMax
* WeibullMaxFactory
* WeibullMaxMuSigma
* GeneralizedExtremeValueFactory
* Pareto
* LeastSquaresDistributionFactory
* ParetoFactory
* DiscreteCompoundDistribution
* Bonmin
* IntersectionEvent
* UnionEvent
* SystemFORM
* MultiFORM
* JoeCopula
* MarginalEvaluation/Gradient/Hessian
* MarshallOlkinCopula
* LinearModelStepwiseAlgorithm
* MixedHistogramUserDefined
* PlackettCopula
* PlackettCopulaFactory
==== API changes ====
* FittingTest methods to return fitted distributions with factory as argument
* Removed deprecated specific RandomVector constructors
* Removed deprecated HypothesisTest::Smirnov
* Removed deprecated FittingTest::TwoSamplesKolmogorov
* Removed deprecated LinearModel, LinearModelFactory
* Removed deprecated HypothesisTest::(Partial|Full)Regression
* Removed deprecated OptimizationProblem(levelFunction, levelValue) ctor
* Removed deprecated (Linear|Quadratic)(LeastSquares|Taylor)::getResponseSurface
* Removed deprecated VisualTest::DrawEmpiricalCDF,DrawHistogram,DrawClouds
* Moved dot to Point::dot
* Deprecated Weibull in favor of WeibullMin
* Deprecated WeibullMuSigma in favor of WeibullMinMuSigma
* Deprecated WeibullFactory in favor of WeibullMinFactory, buildAsWeibull
* Deprecated GumbelAB
* Deprecated GaussianNonLinearCalibration,NonLinearLeastSquaresCalibration::set,getAlgorithm
* Added getConditionalMarginalCovariance method to KrigingResult
* Added getConditionalMarginalVariance method to KrigingResult
* Deprecated OptimizationAlgorithm::GetLeastSquaresAlgorithmNames
* Added linearity features to Function
* Added 'removeKey' method to ResourceMap
* Removed Copula class
* Deprecated Event class, use ThresholdEvent/ProcessEvent/DomainEvent classes
* Deprecated EnumerateFunction constructors
* Added a minimum probability accessor to SubsetSampling
* Added a UniVariateFunction interface to solvers and integration algorithms
=== Python module ===
* Add Domain.__contains__ operator
=== Miscellaneous ===
* Add GeneralizedPareto location parameter
* Add getSobolGroupedTotalIndex method for FunctionalChaosSobolIndices for the indice of a group of variables
=== Bug fixes ===
* #997 (Adding minimum volume set examples)
* #1004 (The doc for SobolSimulationAlgorithm has issues)
* #1006 (Text drawable does not handle size)
* #1130 (Inconsistency in FittingTest)
* #1160 (2-d GaussianProcess realization graph regression)
* #1169 (Missing key in ResourceMap)
* #1173 (There is no dot product example)
* #1185 (Bug with Normal.computeMinimumLevelSet method)
* #1190 (computeProbability clamped by Domain-SmallVolume)
* #1197 (Doc error: TrendTransform)
* #1198 (Doc error: ValueFunction)
* #1202 (Sample::sort & Sample::sortAccordingToAComponent only return new Samples)
* #1204 (sortAccordingToAComponent does not check its inputs arguments)
* #1209 (LinearModelStepwiseAlgorithm from otlm does not exist in OT)
* #1216 (The CalibrationResult doc does not match the code)
* #1247 (KrigingAlgorithm could not compute amplitude analytically with ProductCovarianceModel )
* #1264 (ProductCovarianceModel ignore active parameters of its 1d marginals)
* #1282 (The dlib example fails)
* #1283 (LogNormalFactory::buildMethodOfLeastSquares has no doc)
* #1289 (The help page of PythonFunction has formatting issues)
* #1303 (Rosenblatt transformation segfault)
== 1.13 release (2019-06-06) == #release-1.13
=== Library ===
==== Major changes ====
* Added OPT++ solvers
* Improved a lot the performance of all the Rosenblatt related computations
* Added elementary calibration capabilities
* Added CMinpack, Ceres Solver least-squares solvers
==== New classes ====
* ParametricPointToFieldFunction
* LinearModelResult, LinearModelAlgorithm, LinearModelAnalysis
* OPTpp
* NearestPointProblem, LeastSquaresProblem
* CMinpack
* Ceres
* DiscreteMarkovChain
* CalibrationAlgorithm, CalibrationResult, GaussianLinearCalibration, LinearLeastSquaresCalibration
* NonLinearLeastSquaresCalibration, GaussianNonLinearCalibration
* Hypergeometric