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and present an automatic procedure to compute the optimal reconstruction. This solution&#13;
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                <text>Multiobjective evolutionary algorithms based on decomposition (MOEA/D) represent&#13;
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optimization problems. We introduce the MOEADr package, which offers many of these&#13;
variants as instantiations of a component-oriented framework. This approach contributes&#13;
for easier reproducibility of existing MOEA/D variants from the literature, as well as&#13;
for faster development and testing of new composite algorithms. The package offers an&#13;
standardized, modular implementation of MOEA/D based on this framework, which was&#13;
designed aiming at providing researchers and practitioners with a standard way to discuss&#13;
and express MOEA/D variants. In this paper we introduce the design principles behind&#13;
the MOEADr package, as well as its current components. Three case studies are provided&#13;
to illustrate the main aspects of the package</text>
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                <text>Spatial survival analysis has received a great deal of attention over the last 20 years&#13;
due to the important role that geographical information can play in predicting survival.&#13;
This paper provides an introduction to a set of programs for implementing some Bayesian&#13;
spatial survival models in R using the package spBayesSurv. The function survregbayes&#13;
includes the three most commonly-used semiparametric models: proportional hazards,&#13;
proportional odds, and accelerated failure time. All manner of censored survival times&#13;
are simultaneously accommodated including uncensored, interval censored, current-status,&#13;
left and right censored, and mixtures of these. Left-truncated data are also accommodated. Time-dependent covariates are allowed under the piecewise constant assumption.&#13;
Both georeferenced and areally observed spatial locations are handled via frailties. Model&#13;
fit is assessed with conditional Cox-Snell residual plots, and model choice is carried out via&#13;
the log pseudo marginal likelihood, the deviance information criterion and the WatanabeAkaike information criterion. The accelerated failure time frailty model with a covariatedependent baseline is included in the function frailtyGAFT. In addition, the package&#13;
also provides two marginal survival models: proportional hazards and linear dependent&#13;
Dirichlet process mixtures, where the spatial dependence is modeled via spatial copulas.&#13;
Note that the package can also handle non-spatial data using non-spatial versions of the&#13;
aforementioned models.&#13;
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                <text>Northern Illinois University</text>
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                <text>Fajar bagus W</text>
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                <text> statistical phylogeography, biogeography, population genetics, Bayesian computation, R.</text>
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                <text>BPEC is an R package for Bayesian phylogeographic and ecological clustering which&#13;
allows geographical, environmental and phenotypic measurements to be combined with&#13;
deoxyribonucleic acid (DNA) sequences in order to reveal geographic structuring of DNA&#13;
sequence clusters consistent with migration events. DNA sequences are modelled using a&#13;
collapsed version of a simplified coalescent model projected onto haplotype trees, which&#13;
subsequently give rise to constrained clusterings as migrations occur. Within each cluster, a multivariate Gaussian distribution of the covariates (geographical, environmental,&#13;
phenotypic) is used. Inference follows tailored reversible jump Markov chain Monte Carlo&#13;
sampling so that the number of clusters (i.e., migrations) does not need to be pre-specified.&#13;
A number of output plots and visualizations are provided which reflect the posterior distribution of the parameters of interest. BPEC also includes functions that create output&#13;
files which can be loaded into Google Earth. The package commands are illustrated&#13;
through an example dataset of the polytypic Near Eastern brown frog Rana macrocnemis&#13;
analyzed using BPEC</text>
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                <text>https://www.jstatsoft.org/article/view/v092i05</text>
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                <text>Fajar Bagus W</text>
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                <text>multiplex is a computer program that provides algebraic tools for the analysis of&#13;
multiple network structures within the R environment. Apart from the possibility to create&#13;
and manipulate multivariate data representing multiplex, signed, and two-mode networks,&#13;
this package offers a collection of functions that deal with algebraic systems – such as the&#13;
partially ordered semigroup, and balance or cluster semirings – their decomposition, and&#13;
the enumeration of bundle patterns occurring at different levels of the network. Moreover,&#13;
through Galois derivations between families of the pairs of subsets in different domains&#13;
it is possible to analyze affiliation networks with an algebraic approach. Visualization&#13;
of multigraphs, different forms of bipartite graphs, inclusion lattices, Cayley graphs is&#13;
supported as well with related packages</text>
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                <text>J. Antonio Rivero Ostoic</text>
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                <text>Antonio Rivero Ostoic&#13;
Aarhus University</text>
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                <text>Working with User Agent Strings in Stata: The parseuas Command</text>
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                <text> user agent string, web surveys, device detection, browser, paradata, Stata.&#13;
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            <description>An account of the resource</description>
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                <text>With the rising popularity of web surveys and the increasing use of paradata by survey&#13;
methodologists, assessing information stored in user agent strings becomes inevitable.&#13;
These data contain meaningful information about the browser, operating system, and&#13;
device that a survey respondent uses. This article provides an overview of user agent&#13;
strings, their specific structure and history, how they can be obtained when conducting&#13;
a web survey, as well as what kind of information can be extracted from the strings.&#13;
Further, the user written command parseuas is introduced as an efficient means to gather&#13;
detailed information from user agent strings. The application of parseuas is illustrated&#13;
by an example that draws on a pooled data set consisting of 29 web surveys.</text>
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                <text>Joss Roßmann</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
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                <text>https://www.jstatsoft.org/article/view/v092c01</text>
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            </elementTextContainer>
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          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="86873">
                <text>GESIS – Leibniz Institute&#13;
for the Social Sciences</text>
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          </element>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="86874">
                <text>February 2020</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="37">
            <name>Contributor</name>
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            <elementTextContainer>
              <elementText elementTextId="86875">
                <text>Fajar bagus W</text>
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                <text>Inggris</text>
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                  <text>VOLUME 92 Tahun 2020</text>
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            <element elementId="48">
              <name>Source</name>
              <description>A related resource from which the described resource is derived</description>
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                  <text>https://www.jstatsoft.org/issue/view/v092</text>
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                <text>Computing the Oja Median in R: The Package OjaNP</text>
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                <text>Oja median, Oja signs, Oja signed ranks, Oja ranks, R, C++.&#13;
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                <text>The Oja median is one of several extensions of the univariate median to the multivariate case. It has many desirable properties, but is computationally demanding. In this&#13;
paper, we first review the properties of the Oja median and compare it to other multivariate medians. Then, we discuss four algorithms to compute the Oja median, which&#13;
are implemented in our R package OjaNP. Besides these algorithms, the package contains&#13;
also functions to compute Oja signs, Oja signed ranks, Oja ranks, and the related scatter concepts. To illustrate their use, the corresponding multivariate one- and C-sample&#13;
location tests are implemented.</text>
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                <text>Klaus Nordhausen</text>
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                <text>https://www.jstatsoft.org/article/view/v092i08</text>
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            <name>Publisher</name>
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                <text>Vienna University of Technology</text>
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            <name>Date</name>
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                <text>February 2020</text>
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              <elementText elementTextId="86820">
                <text>Fajar bagus W</text>
              </elementText>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="86821">
                <text>PDF</text>
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          <element elementId="44">
            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="86822">
                <text>Inggris</text>
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            <description>The nature or genre of the resource</description>
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