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XORSHIFT RNGS
Xorshift RNGs George Marsaglia ∗ The Florida State University Abstract Description of a class of simple, extremely fast random number generators (RNGs) with periods 2k −1 for k = 32,64,96,128,160,192. EFFECT DISPLAYS IN R FOR GENERALISED LINEAR MODELS Effect Displays in R for Generalised Linear Models 3 year1998 -0.431179 0.260337 -1.656 0.097674 . year1999 -0.094434 0.261522-0.361 0.718029
JOURNAL OF STATISTICAL SOFTWARE New editorial team members : To sustain the success of JSS in the next years, we have expanded and restructured the editorial team: Rebecca Killick has joined as the fifth Editor-in-Chief, Heidi Seibold became the dedicated Replication Editor for the journal and Reto Stauffer the dedicated Technical Editor. KFAS: EXPONENTIAL FAMILY STATE SPACE MODELS IN R Authors: Jouni Helske: Title: KFAS: Exponential Family State Space Models in R: Abstract: State space modeling is an efficient and flexible method for statistical inference of a broad class of time series and other data. EXTREMES 2.0: AN EXTREME VALUE ANALYSIS PACKAGE IN R Authors: Eric Gilleland, Richard W. Katz: Title: extRemes 2.0: An Extreme Value Analysis Package in R: Abstract: This article describes the extreme value analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. CONDUCTING META-ANALYSES IN R WITH THE METAFOR PACKAGE Authors: Wolfgang Viechtbauer: Title: Conducting Meta-Analyses in R with the metafor Package: Abstract: The metafor package provides functions for conducting meta-analyses in R.The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. MICE: MULTIVARIATE IMPUTATION BY CHAINED EQUATIONS IN R Authors: Stef van Buuren, Karin Groothuis-Oudshoorn: Title: mice: Multivariate Imputation by Chained Equations in R: Abstract: The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. FITTING LINEAR MIXED-EFFECTS MODELS USING LME4 Authors: Douglas Bates, Martin Mächler, Ben Bolker, Steve Walker: Title: Fitting Linear Mixed-Effects Models Using lme4: Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. JM: AN R PACKAGE FOR THE JOINT MODELLING OF LONGITUDINAL 2 JM: Joint Modelling of Longitudinal and Time-to-Event Data in R These two outcomes are often separately analyzed using a mixed e ects model for the longitu- RELATIVE IMPORTANCE FOR LINEAR REGRESSION IN R: THE Journal of Statistical Software 5 to explain, i.e., to compare the R2-values from p regression models with one regressor only. These univariate R2-values are identical to the squared correlations of the regressors with the response. They are available in relaimpo under thename first.
XORSHIFT RNGS
Xorshift RNGs George Marsaglia ∗ The Florida State University Abstract Description of a class of simple, extremely fast random number generators (RNGs) with periods 2k −1 for k = 32,64,96,128,160,192. EFFECT DISPLAYS IN R FOR GENERALISED LINEAR MODELS Effect Displays in R for Generalised Linear Models 3 year1998 -0.431179 0.260337 -1.656 0.097674 . year1999 -0.094434 0.261522-0.361 0.718029
MONITORING DATA IN R WITH THE LUMBERJACK PACKAGE Authors: Mark P. J. van der Loo: Title: Monitoring Data in R with the lumberjack Package: Abstract: Monitoring data while it is processed and transformed can yield detailed insight into the dynamics of a (running) production system. EXTREMES 2.0: AN EXTREME VALUE ANALYSIS PACKAGE IN R Authors: Eric Gilleland, Richard W. Katz: Title: extRemes 2.0: An Extreme Value Analysis Package in R: Abstract: This article describes the extreme value analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. ANALYSIS OF MULTIPLEX SOCIAL NETWORKS WITH R Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK MISSMDA: A PACKAGE FOR HANDLING MISSING VALUES IN Authors: Julie Josse, François Husson: Title: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis: Abstract: We present the R package missMDA which performs principal component methods on incomplete data sets, aiming to obtain scores, loadings and graphical representations despite missing values. LAVAAN: AN R PACKAGE FOR STRUCTURAL EQUATION MODELING Authors: Yves Rosseel: Title: lavaan: An R Package for Structural Equation Modeling: Abstract: Structural equation modeling (SEM) is a vast field and widely used by many applied researchers in the social and behavioral sciences. CONFORMAL PREDICTION WITH ORANGE Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK LOGBIN: AN R PACKAGE FOR RELATIVE RISK REGRESSION USING Authors: Mark W. Donoghoe, Ian C. Marschner: Title: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model: Abstract: Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysisof binary outcomes.
BAYESIAN MULTIVARIATE SPATIAL MODELS FOR LATTICE DATA WITH Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK VAR, SVAR AND SVEC MODELS: IMPLEMENTATION WITHIN R PACKAGE vars_1.4-0.tar.gz: R source package bundle: Download (Downloads: 3567; 455KB) v27i04.R.zip: v27i04.R: R example code from the paper THE R PACKAGE HMI: A CONVENIENT TOOL FOR HIERARCHICAL Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK JOURNAL OF STATISTICAL SOFTWARE New editorial team members : To sustain the success of JSS in the next years, we have expanded and restructured the editorial team: Rebecca Killick has joined as the fifth Editor-in-Chief, Heidi Seibold became the dedicated Replication Editor for the journal and Reto Stauffer the dedicated Technical Editor. LOGBIN: AN R PACKAGE FOR RELATIVE RISK REGRESSION USING10LOG BINOMIAL REGRESSIONR PACKAGE FOR LINEAR REGRESSIONLOGISTIC REGRESSION R PACKAGELINEAR REGRESSION R VALUEREGRESSION R VALUEBINOMIAL LOGISTICREGRESSION MODEL
Authors: Mark W. Donoghoe, Ian C. Marschner: Title: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model: Abstract: Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysisof binary outcomes.
CONTINUOUS ORDINAL REGRESSION FOR ANALYSIS OF VISUALORDINAL REGRESSION INTERPRETATIONORDINAL REGRESSION MODELSORDINAL REGRESSION IN SPSSPYTHON ORDINAL REGRESSIONMULTIPLE REGRESSION DATA SET EXAMPLESMULTIPLE REGRESSION EXAMPLES Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK EXTREMES 2.0: AN EXTREME VALUE ANALYSIS PACKAGE IN R Authors: Eric Gilleland, Richard W. Katz. Title: extRemes 2.0: An Extreme Value Analysis Package in R. Abstract: This article describes the extreme value analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. The functions primarily provide utilities for implementing univariate EVA, with afocus on
VAR, SVAR AND SVEC MODELS: IMPLEMENTATION WITHIN R PACKAGE vars_1.4-0.tar.gz: R source package bundle: Download (Downloads: 3567; 455KB) v27i04.R.zip: v27i04.R: R example code from the paper FITTING LINEAR MIXED-EFFECTS MODELS USING LME4 Fitting Linear Mixed-Effects Models Using lme4. Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula,in
NONPARAMETRIC ECONOMETRICS: THE NP PACKAGE Tristen Hayfield, Jeffrey S. Racine. Title: Nonparametric Econometrics: The np Package. Abstract: We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular amongeconometricians.
MCMC METHODS FOR MULTI-RESPONSE GENERALIZED LINEAR MIXED Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK JM: AN R PACKAGE FOR THE JOINT MODELLING OF LONGITUDINAL 2 JM: Joint Modelling of Longitudinal and Time-to-Event Data in R These two outcomes are often separately analyzed using a mixed e ects model for the longitu-XORSHIFT RNGS
Xorshift RNGs George Marsaglia ∗ The Florida State University Abstract Description of a class of simple, extremely fast random number generators (RNGs) with periods 2k −1 for k = 32,64,96,128,160,192. JOURNAL OF STATISTICAL SOFTWARE New editorial team members : To sustain the success of JSS in the next years, we have expanded and restructured the editorial team: Rebecca Killick has joined as the fifth Editor-in-Chief, Heidi Seibold became the dedicated Replication Editor for the journal and Reto Stauffer the dedicated Technical Editor. LOGBIN: AN R PACKAGE FOR RELATIVE RISK REGRESSION USING10LOG BINOMIAL REGRESSIONR PACKAGE FOR LINEAR REGRESSIONLOGISTIC REGRESSION R PACKAGELINEAR REGRESSION R VALUEREGRESSION R VALUEBINOMIAL LOGISTICREGRESSION MODEL
Authors: Mark W. Donoghoe, Ian C. Marschner: Title: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model: Abstract: Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysisof binary outcomes.
CONTINUOUS ORDINAL REGRESSION FOR ANALYSIS OF VISUALORDINAL REGRESSION INTERPRETATIONORDINAL REGRESSION MODELSORDINAL REGRESSION IN SPSSPYTHON ORDINAL REGRESSIONMULTIPLE REGRESSION DATA SET EXAMPLESMULTIPLE REGRESSION EXAMPLES Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK EXTREMES 2.0: AN EXTREME VALUE ANALYSIS PACKAGE IN R Authors: Eric Gilleland, Richard W. Katz. Title: extRemes 2.0: An Extreme Value Analysis Package in R. Abstract: This article describes the extreme value analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. The functions primarily provide utilities for implementing univariate EVA, with afocus on
VAR, SVAR AND SVEC MODELS: IMPLEMENTATION WITHIN R PACKAGE vars_1.4-0.tar.gz: R source package bundle: Download (Downloads: 3567; 455KB) v27i04.R.zip: v27i04.R: R example code from the paper FITTING LINEAR MIXED-EFFECTS MODELS USING LME4 Fitting Linear Mixed-Effects Models Using lme4. Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula,in
NONPARAMETRIC ECONOMETRICS: THE NP PACKAGE Tristen Hayfield, Jeffrey S. Racine. Title: Nonparametric Econometrics: The np Package. Abstract: We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular amongeconometricians.
MCMC METHODS FOR MULTI-RESPONSE GENERALIZED LINEAR MIXED Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK JM: AN R PACKAGE FOR THE JOINT MODELLING OF LONGITUDINAL 2 JM: Joint Modelling of Longitudinal and Time-to-Event Data in R These two outcomes are often separately analyzed using a mixed e ects model for the longitu-XORSHIFT RNGS
Xorshift RNGs George Marsaglia ∗ The Florida State University Abstract Description of a class of simple, extremely fast random number generators (RNGs) with periods 2k −1 for k = 32,64,96,128,160,192. MONITORING DATA IN R WITH THE LUMBERJACK PACKAGE Authors: Mark P. J. van der Loo: Title: Monitoring Data in R with the lumberjack Package: Abstract: Monitoring data while it is processed and transformed can yield detailed insight into the dynamics of a (running) production system. ANALYSIS OF MULTIPLEX SOCIAL NETWORKS WITH R Authors: Matteo Magnani, Luca Rossi, Davide Vega: Title: Analysis of Multiplex Social Networks with R: Abstract: Multiplex social networks are characterized by a common set of actors connected through multipletypes of relations.
THE R PACKAGE HMI: A CONVENIENT TOOL FOR HIERARCHICAL Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK CONTINUOUS ORDINAL REGRESSION FOR ANALYSIS OF VISUAL Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK NONPARAMETRIC MACHINE LEARNING AND EFFICIENT COMPUTATION Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package LMERTEST PACKAGE: TESTS IN LINEAR MIXED EFFECTS MODELS Authors: Alexandra Kuznetsova, Per B. Brockhoff, Rune H. B. Christensen: Title: lmerTest Package: Tests in Linear Mixed Effects Models: Abstract: One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? STAN: A PROBABILISTIC PROGRAMMING LANGUAGE Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlomethods such as
BAYESIANMULTIVARIATESPATIALMODELSFORLATTICE DATAWITH INLA JSS JournalofStatisticalSoftware May2021,Volume98,Issue2.doi:10.18637/jss.v098.i02 BayesianMultivariateSpatialModelsforLattice DatawithINLA FranciscoPalmí-Perales GPFIT: AN R PACKAGE FOR FITTING A GAUSSIAN PROCESS MODEL Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK KALMAN FILTERING IN R Kalman Filtering in R. Abstract: Support in R for state space estimation via Kalman filtering was limited to one package, until fairly recently. In the last five years, the situation has changed with no less than four additional packages offering general implementations of the Kalman filter, including in some cases smoothing, simulation JOURNAL OF STATISTICAL SOFTWARE New editorial team members : To sustain the success of JSS in the next years, we have expanded and restructured the editorial team: Rebecca Killick has joined as the fifth Editor-in-Chief, Heidi Seibold became the dedicated Replication Editor for the journal and Reto Stauffer the dedicated Technical Editor. EXTREMES 2.0: AN EXTREME VALUE ANALYSIS PACKAGE IN R265 Authors: Eric Gilleland, Richard W. Katz. Title: extRemes 2.0: An Extreme Value Analysis Package in R. Abstract: This article describes the extreme value analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. The functions primarily provide utilities for implementing univariate EVA, with afocus on
LOGBIN: AN R PACKAGE FOR RELATIVE RISK REGRESSION USINGLOG BINOMIAL REGRESSIONR PACKAGE FOR LINEAR REGRESSIONLOGISTIC REGRESSION R PACKAGELINEAR REGRESSION R VALUEREGRESSION R VALUEBINOMIAL LOGISTICREGRESSION MODEL
Authors: Mark W. Donoghoe, Ian C. Marschner: Title: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model: Abstract: Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysisof binary outcomes.
FITTING LINEAR MIXED-EFFECTS MODELS USING LME4 Fitting Linear Mixed-Effects Models Using lme4. Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula,in
SUPERVISED MULTIBLOCK ANALYSIS IN R WITH THE ADE4 PACKAGE Authors: Stéphanie Bougeard, Stéphane Dray: Title: Supervised Multiblock Analysis in R with the ade4 Package: Abstract: This paper presents two novel statistical analyses of DTWSAT: TIME-WEIGHTED DYNAMIC TIME WARPING FOR SATELLITE Authors: Victor Maus, Gilberto Câmara, Marius Appel, Edzer Pebesma: Title: dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R MCMC METHODS FOR MULTI-RESPONSE GENERALIZED LINEAR MIXED Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK CONDUCTING META-ANALYSES IN R WITH THE METAFOR PACKAGE Authors: Wolfgang Viechtbauer: Title: Conducting Meta-Analyses in R with the metafor Package: Abstract: The metafor package provides functions for conducting meta-analyses in R.The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. JM: AN R PACKAGE FOR THE JOINT MODELLING OF LONGITUDINAL 2 JM: Joint Modelling of Longitudinal and Time-to-Event Data in R These two outcomes are often separately analyzed using a mixed e ects model for the longitu- EXTREMES 2.0: ANEXTREMEVALUEANALYSISPACKAGE IN R 2 extRemes 2.0: An Extreme Value Analysis Package in R weather and climate science communities. Over the years, extRemes garnered a largeuser
JOURNAL OF STATISTICAL SOFTWARE New editorial team members : To sustain the success of JSS in the next years, we have expanded and restructured the editorial team: Rebecca Killick has joined as the fifth Editor-in-Chief, Heidi Seibold became the dedicated Replication Editor for the journal and Reto Stauffer the dedicated Technical Editor. EXTREMES 2.0: AN EXTREME VALUE ANALYSIS PACKAGE IN R265 Authors: Eric Gilleland, Richard W. Katz. Title: extRemes 2.0: An Extreme Value Analysis Package in R. Abstract: This article describes the extreme value analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. The functions primarily provide utilities for implementing univariate EVA, with afocus on
LOGBIN: AN R PACKAGE FOR RELATIVE RISK REGRESSION USINGLOG BINOMIAL REGRESSIONR PACKAGE FOR LINEAR REGRESSIONLOGISTIC REGRESSION R PACKAGELINEAR REGRESSION R VALUEREGRESSION R VALUEBINOMIAL LOGISTICREGRESSION MODEL
Authors: Mark W. Donoghoe, Ian C. Marschner: Title: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model: Abstract: Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysisof binary outcomes.
FITTING LINEAR MIXED-EFFECTS MODELS USING LME4 Fitting Linear Mixed-Effects Models Using lme4. Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula,in
SUPERVISED MULTIBLOCK ANALYSIS IN R WITH THE ADE4 PACKAGE Authors: Stéphanie Bougeard, Stéphane Dray: Title: Supervised Multiblock Analysis in R with the ade4 Package: Abstract: This paper presents two novel statistical analyses of DTWSAT: TIME-WEIGHTED DYNAMIC TIME WARPING FOR SATELLITE Authors: Victor Maus, Gilberto Câmara, Marius Appel, Edzer Pebesma: Title: dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R MCMC METHODS FOR MULTI-RESPONSE GENERALIZED LINEAR MIXED Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK CONDUCTING META-ANALYSES IN R WITH THE METAFOR PACKAGE Authors: Wolfgang Viechtbauer: Title: Conducting Meta-Analyses in R with the metafor Package: Abstract: The metafor package provides functions for conducting meta-analyses in R.The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. JM: AN R PACKAGE FOR THE JOINT MODELLING OF LONGITUDINAL 2 JM: Joint Modelling of Longitudinal and Time-to-Event Data in R These two outcomes are often separately analyzed using a mixed e ects model for the longitu- EXTREMES 2.0: ANEXTREMEVALUEANALYSISPACKAGE IN R 2 extRemes 2.0: An Extreme Value Analysis Package in R weather and climate science communities. Over the years, extRemes garnered a largeuser
MONITORING DATA IN R WITH THE LUMBERJACK PACKAGE Authors: Mark P. J. van der Loo: Title: Monitoring Data in R with the lumberjack Package: Abstract: Monitoring data while it is processed and transformed can yield detailed insight into the dynamics of a (running) production system. THE R PACKAGE HMI: A CONVENIENT TOOL FOR HIERARCHICAL Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK ANALYSIS OF MULTIPLEX SOCIAL NETWORKS WITH R Authors: Matteo Magnani, Luca Rossi, Davide Vega: Title: Analysis of Multiplex Social Networks with R: Abstract: Multiplex social networks are characterized by a common set of actors connected through multipletypes of relations.
VAR, SVAR AND SVEC MODELS: IMPLEMENTATION WITHIN R PACKAGE vars_1.4-0.tar.gz: R source package bundle: Download (Downloads: 3567; 455KB) v27i04.R.zip: v27i04.R: R example code from the paper LMERTEST PACKAGE: TESTS IN LINEAR MIXED EFFECTS MODELS Authors: Alexandra Kuznetsova, Per B. Brockhoff, Rune H. B. Christensen: Title: lmerTest Package: Tests in Linear Mixed Effects Models: Abstract: One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? STAN: A PROBABILISTIC PROGRAMMING LANGUAGE Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlomethods such as
CONDUCTING META-ANALYSES IN R WITH THE METAFOR PACKAGE Authors: Wolfgang Viechtbauer: Title: Conducting Meta-Analyses in R with the metafor Package: Abstract: The metafor package provides functions for conducting meta-analyses in R.The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. BAYESIANMULTIVARIATESPATIALMODELSFORLATTICE DATAWITH INLA JSS JournalofStatisticalSoftware May2021,Volume98,Issue2.doi:10.18637/jss.v098.i02 BayesianMultivariateSpatialModelsforLattice DatawithINLA FranciscoPalmí-Perales GLOTARAN: A JAVA-BASED GRAPHICAL USER INTERFACE FOR THE R Authors: Joris J. Snellenburg, Sergey Laptenok, Ralf Seger, Katharine M. Mullen, Ivo H. M. van Stokkum: Title: Glotaran: A Java-Based Graphical User Interface for the FLEXSURV: A PLATFORM FOR PARAMETRIC SURVIVAL MODELING IN R Authors: Christopher Jackson: Title: flexsurv: A Platform for Parametric Survival Modeling in R: Abstract: flexsurv is an R package for fully-parametric modeling of survival data. Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis ISSN 1548-7660; CODEN JSSOBK Open Journal SystemsJournal Help
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Home > Vol 93 (2020)RECENT PUBLICATIONS
ARTICLES
ManifoldOptim: An R Interface to the ROPTLIB Library for Riemannian Manifold OptimizationThe R Package JSM
R
Models
Clara Happ-Kurz
Bayesian Random-Effects Meta-Analysis Using the bayesmeta R PackageChristian Röver
lslx: Semi-Confirmatory Structural Equation Modeling via PenalizedLikelihood
Po-Hsien Huang
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional DataHACopula Toolbox
Missingness
Scharfstein
webchem: An R Package to Retrieve Chemical Information from the WebCODE SNIPPETS
Analysis of Archaeological Phases Using the R Package ArchaeoPhasesBOOK REVIEWS
Flexible Imputation of Missing Data (2nd Edition)Abdolvahab Khademi
Established in 1996, the Journal of Statistical Software publishes articles, book reviews, code snippets, and software reviews on the subject of statistical software and algorithms. The contents are freely available on-line. For both articles and code snippets the source code is published along with the paper. Statistical software is the key link between statistical methods and their application in practice. Software that makes this link is the province of the journal, and may be realized as, for instance, tools for large scale computing, database technology, desktop computing, distributed systems, the World Wide Web, reproducible research, archiving and documentation, and embedded systems. We attempt to present research that demonstrates the joint evolution of computational and statistical methods and techniques. Implementations can use languages such as C, C++, S, Fortran, Java, PHP, Python and Ruby or environments such as Mathematica, MATLAB, R, S-PLUS, SAS, Stata, and XLISP-STAT.ANNOUNCEMENTS
CHANGES IN EDITORIAL TEAM In response to the continuing success of JSS we have expanded our editorial team aiming to further enhance the quality of publications and lower reviewing times and publication delays. First, Torsten Hothorn has joined as the fourth editor-in-chief. Second, we now have four editorial assistants who help with managing submissions, work flows, editing, technical checks, web services, etc.: Aaron Danielson, Gregor Kastner, Heidi Seibold, Reto Stauffer. Welcome to all the new members of the editorial team! JSS CELEBRATES 20 YEARS: SPECIAL VOLUME 73 2016 marks the 20th anniversary of the Journal of Statistical Software. We celebrate the anniversary with a FESTSCHRIFT FOR JAN DE LEEUW, the journal's founding editor, which is now online as Special Volume 73 _(Editors: Patrick Mair, KatharineMullen)_.
NEW JSS SERVER
The _Journal of Statistical Software_ moved to a new server based on _Open Journal Systems_ (OJS, http://pkp.sfu.ca/ojs/) which not only hosts published software/manuscripts but also provides a full editorial system. Thus, all submissions, reviews, etc. can now be made through the new OJS system. More Announcements...SUPPORT
As a matter of principle, JSS CHARGES NO AUTHOR FEES OR SUBSCRIPTION FEES. Our editors, reviewers, and programmers are volunteers. UCLA Statistics and Universität Innsbruck contribute support staff, website maintenance, website hosting, and some graduate student support. Because of our success and growth we do need more resources in the future. You can support us by becoming a member of the Foundation for Open Access Statistics at www.foastat.org , and by contributing on theirdonation page .
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ISSN: 1548-7660
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