Search results for "lcsh:Statistics"

showing 10 items of 22 documents

Dynamic-Interactive Graphics for Statistics (26 years later)

2014

This paper briefly reviews the history of dynamic-interactive graphicsfor statistics, introduces an example of such graphics, and provides a fewglimpses as to the current state of things and the future trends we envision.The general conclusion is that dynamic-interactive graphics for statistics arethriving more than ever as they shift from the desktop to the internet. Thus,dynamic-interactive graphics are becoming increasingly important as they: 1) provide non-experts in statistics with the means to carry out analyses on their own; and 2) teach the basic concepts of statistics to students and practitioners with low to moderate  mathematics skills. Their increasingpopularity makes the lesson…

Statistics and ProbabilityDYNAMIC-GRAPHICS31 Colecciones de estadística general / StatisticsInteractive graphics//purl.org/becyt/ford/1 [https]Data visualizationStatisticsGraphicsGráficas dinámicasStatistical graphicslcsh:Statisticslcsh:HA1-4737business.industryData VisualizationSubject (documents)Dynamic Graphics//purl.org/becyt/ford/1.2 [https]PopularitySTATISTICSStatistical Graphicsgráficas dinámicas51 Matemáticas / MathematicsVisualizaciónvisualizaciónCiencias de la Computación e InformaciónThrivingThe InternetDATA-VISUALIZATIONbusinessgráficas estadísticasGráficas estadísticasCiencias de la Información y BioinformáticaCIENCIAS NATURALES Y EXACTAS
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Analyzing Temperature Effects on Mortality Within theREnvironment: The Constrained Segmented Distributed Lag Parameterization

2010

Here we present and discuss the R package modTempEff including a set of functions aimed at modelling temperature effects on mortality with time series data. The functions fit a particular log linear model which allows to capture the two main features of mortality- temperature relationships: nonlinearity and distributed lag effect. Penalized splines and segmented regression constitute the core of the modelling framework. We briefly review the model and illustrate the functions throughout a simulated dataset.

Statistics and ProbabilityDistributed lagtemperature effects segmented relationship break point P-splines RMathematical optimizationComputer scienceP-splinesRsegmented relationshipSet (abstract data type)R packageNonlinear systemBreak pointApplied mathematicsLog-linear modelbreak pointStatistics Probability and UncertaintySegmented regressionTime seriesSettore SECS-S/01 - Statisticatemperature effectslcsh:Statisticslcsh:HA1-4737SoftwareJournal of Statistical Software
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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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R Graphics (3rd Edition)

2020

Statistics and ProbabilityEngineeringbusiness.industryComputer graphics (images)Statistics Probability and UncertaintyGraphicsbusinesslcsh:Statisticslcsh:HA1-4737SoftwareJournal of Statistical Software
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A Software Tool for the Exponential Power Distribution: The normalp Package

2005

In this paper we present the normalp package, a package for the statistical environment R that has a set of tools for dealing with the exponential power distribution. In this package there are functions to compute the density function, the distribution function and the quantiles from an exponential power distribution and to generate pseudo-random numbers from the same distribution. Moreover, methods concerning the estimation of the distribution parameters are described and implemented. It is also possible to estimate linear regression models when we assume the random errors distributed according to an exponential power distribution. A set of functions is designed to perform simulation studi…

Statistics and ProbabilityExponential distributionTheoretical computer scienceComputer scienceAsymptotic distributionDistribution fittingLaplace distributionExponential familyGamma distributionStatistics Probability and UncertaintyNatural exponential familyProbability integral transformAlgorithmlcsh:Statisticslcsh:HA1-4737exponential power distribution R estimation linear regressionSoftwareJournal of Statistical Software
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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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Data Analysis Using Hierarchical Generalized Linear Models with R

2019

Statistics and ProbabilityGeneralized linear modelApplied mathematicsStatistics Probability and Uncertaintylcsh:Statisticslcsh:HA1-4737SoftwareMathematicsJournal of Statistical Software
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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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Humanities Data inR

2016

Statistics and ProbabilityLibrary scienceSociologyStatistics Probability and Uncertaintylcsh:Statisticslcsh:HA1-4737SoftwareJournal of Statistical Software
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Using R via PHP for Teaching Purposes: R-php

2006

This paper deals with the R-php statistical software, that is an environment for statistical analysis, freely accessible and attainable through the World Wide Web, based on R. Indeed, this software uses, as "engine" for statistical analyses, R via PHP and its design has been inspired by a paper of de Leeuw (1997). R-php is based on two modules: a base module and a point-and-click module. R-php base allows the simple editing of R code in a form. R-php point-and-click allows some statistical analyses by means of a graphical user interface (GUI): then, to use this module it is not necessary for the user to know the R environment, but all the allowed analyses can be performed by using the compu…

Statistics and ProbabilitySIMPLE (military communications protocol)business.industryProgramming languageComputer scienceComputer laboratoryRstatistical software R PHP graphical user interfacePHPBase (topology)computer.software_genreSoftwareHuman–computer interactionStatistical analysisstatistical softwareStatistics Probability and UncertaintyComputer mousebusinessgraphical user interface.computerlcsh:Statisticslcsh:HA1-4737SoftwareStatistical softwareGraphical user interfaceJournal of Statistical Software
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