Search results for "computer.programming_language"

showing 10 items of 986 documents

Water absorption and hydrothermal performance of PHBV/sisal biocomposites

2014

[EN] The performance of biocomposites of poly(hydroxybutyrate-co-valerate) (PHBV) and sisal fibre subjected to hydrothermal tests at different temperatures above the glass transition of PHBV (TH ¿ 26, 36 and 46 C) was evaluated in this study. The influences of both the fibre content and presence of coupling agent were focused. The water absorption capability and water diffusion rate were considered for a statistical factorial analysis. Afterwards, the physico-chemical properties of water-saturated biocomposites were assessed by Fourier-Transform Infrared Analysis, Size Exclusion Chromatography, Differential Scanning Calorimetry and Scanning Electron Microscopy. It was found that the water d…

Statistical factorial analysis (SFA)Materials scienceAbsorption of waterBiocompositesPolymers and PlasticsMaterials compostosTermoplàsticsCondensed Matter PhysicsHydrothermal degradationHydrothermal circulationLignocellulosic fibresSisalPoly(hydroxybutyrate-co-valerate) (PHBV)Mechanics of MaterialsMAQUINAS Y MOTORES TERMICOSMaterials ChemistrySisal fibreComposite materialGlass transitioncomputerSISALcomputer.programming_language
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A Comparison of Formulae for Calculating Cost-Efficient Sample Sizes of Case-Control Studies with an Internal Validation Scheme

2000

When a case-control study is planned to include an internal validation study, the sample size of the study and the proportion of validated observations has to be calculated. There are a variety of alternative methods to accomplish this. In this article some possible procedures will be compared in order to clarify whether considerable differences in the suggested optimal designs occur, dependent on the used method.

Statistics and ProbabilityAlternative methodsScheme (programming language)Optimal designMathematical optimizationCost efficiencyEstimation theoryComputer scienceSmall sampleGeneral MedicineSample size determinationStatisticsStatistics Probability and UncertaintyInternal validationcomputercomputer.programming_languageBiometrical Journal
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Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp

2017

Blind source separation (BSS) is a well-known signal processing tool which is used to solve practical data analysis problems in various fields of science. In BSS, we assume that the observed data consists of linear mixtures of latent variables. The mixing system and the distributions of the latent variables are unknown. The aim is to find an estimate of an unmixing matrix which then transforms the observed data back to latent sources. In this paper we present the R packages JADE and BSSasymp. The package JADE offers several BSS methods which are based on joint diagonalization. Package BSSasymp contains functions for computing the asymptotic covariance matrices as well as their data-based es…

Statistics and ProbabilityComputer scienceJADE (programming language)02 engineering and technologyLatent variableMachine learningcomputer.software_genre01 natural sciencesBlind signal separation010104 statistics & probabilityMatrix (mathematics)nonstationary source separationMixing (mathematics)0202 electrical engineering electronic engineering information engineeringsecond order source separation0101 mathematicslcsh:Statisticslcsh:HA1-4737computer.programming_languageta113Signal processingta112matematiikkamultivariate time seriesmathematicsbusiness.industryEstimator020206 networking & telecommunicationsriippumattomien komponenttien analyysiindependent component analysis; multivariate time series; nonstationary source separation; performance indices; second order source separationIndependent component analysisperformance indicesstatisticsindependent component analysisArtificial intelligenceStatistics Probability and UncertaintybusinesscomputerAlgorithmSoftwareJournal of Statistical Software
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Functional Data Analysis with R and Matlab by RAMSAY, J. O., HOOKER, G., and GRAVES, S.

2010

Statistics and ProbabilityDiscrete mathematicsGeneral Immunology and MicrobiologyApplied MathematicsFunctional data analysisGeneral MedicineGeneral Agricultural and Biological SciencesMATLABcomputerGeneral Biochemistry Genetics and Molecular BiologyDemographyMathematicscomputer.programming_languageBiometrics
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Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes’ Description

2017

etasFLP is an R package which fits an epidemic type aftershock sequence (ETAS) model to an earthquake catalog; non-parametric background seismicity can be estimated through a forward predictive likelihood approach, while parametric components of triggered seismicity are estimated through maximum likelihood; estimation steps are alternated until convergence is obtained and for each event the probability of being a background event is estimated. The package includes options which allow its wide use. Methods for plot, summary and profile are defined for the main output class object. The paper provides examples of the package's use with description of the underlying R and Fortran routines.

Statistics and ProbabilityEarthquakeComputer scienceFortranFortranInduced seismicity010502 geochemistry & geophysicscomputer.software_genre01 natural sciencesPlot (graphics)Point processPhysics::GeophysicsPoint proce010104 statistics & probabilityetasFLP; R; Fortran; point process; ETAS; earthquakesETAS0101 mathematicsearthquakeslcsh:Statisticslcsh:HA1-4737AftershockEtasFLPpoint process0105 earth and related environmental sciencesEvent (probability theory)Parametric statisticscomputer.programming_languageNonparametric statisticsRetasFLP R Fortran point process ETAS earthquakes.Data miningStatistics Probability and UncertaintySettore SECS-S/01 - StatisticacomputerAlgorithmSoftware
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Introducing libeemd: a program package for performing the ensemble empirical mode decomposition

2016

The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD). All these methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series data, and provides a way to separate short time-scale events from a general trend. We present a free software implementation of EMD, EEMD and CEEMDAN and give an overview of the EMD methodology and the algorithms used in the deco…

Statistics and ProbabilityFOS: Computer and information sciences010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologies02 engineering and technology01 natural sciencesExtensibilityStatistics - ComputationHilbert–Huang transformSoftware implementationHilbert–Huang transformSannolikhetsteori och statistikTime seriesProbability Theory and StatisticsComputation (stat.CO)021101 geological & geomatics engineering0105 earth and related environmental sciencescomputer.programming_languagenoise-assisted data analysisintrinsic mode functionPython (programming language)adaptive data analysisComputational MathematicsNonlinear systemtime series analysisData analysisStatistics Probability and UncertaintyAlgorithmcomputerdetrendingHilbert-Huang transform; Intrinsic mode function; Time series analysis; Adaptive data analysis; Noise-assisted data analysis; Detrending
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Identifying Causal Effects with the R Package causaleffect

2017

Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately eit…

Statistics and ProbabilityFOS: Computer and information sciencesTheoretical computer sciencecausalityDistribution (number theory)C-componentComputer sciencecausal model02 engineering and technologyCausal structureMethodology (stat.ME)03 medical and health sciences0302 clinical medicinedo-calculusJoint probability distribution0202 electrical engineering electronic engineering information engineering030212 general & internal medicineDAG; do-calculus; causality; causal model; identifiability; graph; C-component; hedge; d-separationlcsh:Statisticslcsh:HA1-4737Statistics - Methodologycomputer.programming_languageCausal modelta112DAGd-separationgraphhedgeidentifiabilityExpression (mathematics)PEARL (programming language)Action (philosophy)kausaliteetti020201 artificial intelligence & image processingStatistics Probability and UncertaintycomputerSoftware
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dglars: An R Package to Estimate Sparse Generalized Linear Models

2014

dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013), developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004). The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013), and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012). The latter algorithm, as shown here, is significan…

Statistics and ProbabilityGeneralized linear modelEXPRESSIONMathematical optimizationTISSUESFortrancyclic coordinate descent algorithmdgLARSFeature selectionDANTZIG SELECTORpredictor-corrector algorithmLIKELIHOODLEAST ANGLE REGRESSIONsparse modelsDifferential (infinitesimal)differential geometrylcsh:Statisticslcsh:HA1-4737computer.programming_languageMathematicsLeast-angle regressionExtension (predicate logic)Expression (computer science)generalized linear modelsBREAST-CANCER RISKVARIABLE SELECTIONDifferential geometrydifferential geometry generalized linear models dgLARS predictor-corrector algorithm cyclic coordinate descent algorithm sparse models variable selection.MARKERSHRINKAGEStatistics Probability and UncertaintyHAPLOTYPESSettore SECS-S/01 - StatisticacomputerAlgorithmSoftware
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On surrogating 0–1 knapsack constraints

1999

In this note, we present a scheme for tightening 0–1 knapsack constraints based on other knapsack constraints surrogating.

Statistics and ProbabilityScheme (programming language)Mathematical optimizationInformation Systems and ManagementKnapsack problemModeling and SimulationCalculusDiscrete Mathematics and CombinatoricsManagement Science and Operations Researchcomputercomputer.programming_languageMathematicsTop
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NeoFox: annotating neoantigen candidates with neoantigen features

2020

Abstract Summary The detection and prediction of true neoantigens is of great importance for the field of cancer immunotherapy. Wesearched the literature for proposed neoantigen features and integrated them into a toolbox called NEOantigen Feature toolbOX (NeoFox). NeoFox is an easy-to-use Python package that enables the annotation of neoantigen candidates with 16 neoantigen features. Availability and implementation NeoFox is freely available as an open source Python package released under the GNU General Public License (GPL) v3 license at https://github.com/TRON-Bioinformatics/neofox. Supplementary information Supplementary data are available at Bioinformatics online.

Statistics and ProbabilitySupplementary data0303 health sciencesInformation retrievalComputer science030302 biochemistry & molecular biologyPython (programming language)BiochemistryToolbox3. Good healthComputer Science Applications03 medical and health sciencesComputational MathematicsAnnotationOpen sourceComputational Theory and MathematicsMolecular Biologycomputer030304 developmental biologycomputer.programming_languageBioinformatics
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