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                <text>Blang: Bayesian Declarative Modeling of General Data Structures and Inference via Algorithms Based on Distribution Continua</text>
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                <text>Bayesian modeling language, Bayesian inference, non-standard data structures,&#13;
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                <text>Consider a Bayesian inference problem where a variable of interest does not take&#13;
values in a Euclidean space. These “non-standard” data structures are in reality fairly&#13;
common. They are frequently used in problems involving latent discrete factor models,&#13;
networks, and domain specific problems such as sequence alignments and reconstructions,&#13;
pedigrees, and phylogenies. In principle, Bayesian inference should be particularly wellsuited in such scenarios, as the Bayesian paradigm provides a principled way to obtain&#13;
confidence assessment for random variables of any type. However, much of the recent work&#13;
on making Bayesian analysis more accessible and computationally efficient has focused on&#13;
inference in Euclidean spaces.&#13;
In this paper, we introduce Blang, a domain specific language and library aimed at&#13;
bridging this gap. Blang allows users to perform Bayesian analysis on arbitrary data types&#13;
while using a declarative syntax similar to the popular family of probabilistic programming&#13;
languages, BUGS. Blang is augmented with intuitive language additions to create data&#13;
types of the user’s choosing. To perform inference at scale on such arbitrary state spaces,&#13;
Blang leverages recent advances in sequential Monte Carlo and non-reversible Markov&#13;
chain Monte Carlo methods</text>
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                <text>https://www.jstatsoft.org/article/view/v103i11</text>
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                <text>Robust Mediation Analysis: The R Package robmed</text>
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                <text> mediation analysis, robust statistics, bootstrap, R.</text>
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                <text>Mediation analysis is one of the most widely used statistical techniques in the social,&#13;
behavioral, and medical sciences. Mediation models allow to study how an independent&#13;
variable affects a dependent variable indirectly through one or more intervening variables,&#13;
which are called mediators. The analysis is often carried out via a series of linear regressions, in which case the indirect effects can be computed as products of coefficients from&#13;
those regressions. Statistical significance of the indirect effects is typically assessed via a&#13;
bootstrap test based on ordinary least-squares estimates. However, this test is sensitive to&#13;
outliers or other deviations from normality assumptions, which poses a serious threat to&#13;
empirical testing of theory about mediation mechanisms. The R package robmed implements a robust procedure for mediation analysis based on the fast-and-robust bootstrap&#13;
methodology for robust regression estimators, which yields reliable results even when the&#13;
data deviate from the usual normality assumptions. Various other procedures for mediation analysis are included in package robmed as well. Moreover, robmed introduces a&#13;
new formula interface that allows to specify mediation models with a single formula, and&#13;
provides various plots for diagnostics or visual representation of the results.</text>
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                <text>Andreas Alfons</text>
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                <text>https://www.jstatsoft.org/article/view/v103i13</text>
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                <text>This paper describes and illustrates functionality of the spNNGP R package. The&#13;
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chain Monte Carlo (MCMC) and MCMC-free nearest neighbor Gaussian process (NNGP)&#13;
models for inference about large spatial data. Non-Gaussian outcomes are modeled using&#13;
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take advantage of multiprocessor systems. Package features are illustrated using simulated&#13;
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                <text>evgam: An R Package for Generalized Additive Extreme Value Models</text>
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                <text>: generalized extreme value distribution, generalized Pareto distribution, point process, generalized additive model, Laplace’s method, R</text>
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                <text>This article introduces the R package evgam. The package provides functions for&#13;
fitting extreme value distributions. These include the generalized extreme value and&#13;
generalized Pareto distributions. The former can also be fitted through a point process&#13;
representation. Package evgam supports quantile regression via the asymmetric Laplace&#13;
distribution, which can be useful for estimating high thresholds, sometimes used to discriminate between extreme and non-extreme values. The main addition of package evgam&#13;
is to let extreme value distribution parameters have generalized additive model forms, the&#13;
smoothness of which can be objectively estimated using Laplace’s method. Illustrative&#13;
examples fitting various distributions with various specifications are given. These include&#13;
daily precipitation accumulations for part of Colorado, US, used to illustrate spatial models, and daily maximum temperatures for Fort Collins, Colorado, US, used to illustrate&#13;
temporal models</text>
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                <text>Benjamin D. Youngman</text>
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                <text>University of Exeter</text>
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                <text>Fajar bagus W</text>
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                  <text>VOLUME 103 Tahun 2022</text>
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                <text>Feller-Pareto and Related Distributions: Numerical Implementation and Actuarial Applications</text>
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                <text> Feller-Pareto, distribution, transformed beta, beta of the second kind, actuar, R.</text>
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                <text>Actuaries model insurance claim amounts using heavy tailed probability distributions.&#13;
They routinely need to evaluate quantities related to these distributions such as quantiles&#13;
in the far right tail, moments or limited moments. Furthermore, actuaries often resort&#13;
to simulation to solve otherwise untractable risk evaluation problems. The paper discusses our implementation of support functions for the Feller-Pareto distribution for the&#13;
R package actuar. The Feller-Pareto defines a large family of heavy tailed distributions&#13;
encompassing the transformed beta family and many variants of the Pareto distribution.</text>
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                <text>Christophe Dutang</text>
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            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="88529">
                <text>https://www.jstatsoft.org/article/view/v103i06</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="88530">
                <text>Université Paris-Dauphine&#13;
</text>
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                <text>Fajar bagus W</text>
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                  <text>VOLUME 103 Tahun 2022</text>
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                <text>Automatic Identification and Forecasting of Structural Unobserved Components Models with UComp</text>
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          <element elementId="49">
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            <description>The topic of the resource</description>
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                <text>unobserved components models, state space models, Kalman filter, fixed point&#13;
smoother, maximum likelihood, R, MATLAB, Octave.</text>
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                <text>UComp is a powerful library for building unobserved components models, useful for&#13;
forecasting and other important operations, such us de-trending, cycle analysis, seasonal&#13;
adjustment, signal extraction, etc. One of the most outstanding features that makes&#13;
UComp unique among its class of related software implementations is that models may&#13;
be built automatically by identification algorithms (three versions are available). These&#13;
algorithms select the best model among many possible combinations. Another relevant&#13;
feature is that it is coded in C++, opening the door to link it to different popular and&#13;
widely used environments, like R, MATLAB, Octave, Python, etc. The implemented models for the components are more general than the usual ones in the field of unobserved&#13;
components modeling, including different types of trend, cycle, seasonal and irregular components, input variables and outlier detection. The automatic character of the algorithms&#13;
required the development of many complementary algorithms to control performance and&#13;
make it applicable to as many different time series as possible. The library is open source&#13;
and available in different formats in public repositories. The performance of the library&#13;
is illustrated working on real data in several varied examples.</text>
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                <text>Diego J. Pedregal</text>
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              <elementText elementTextId="88562">
                <text>https://www.jstatsoft.org/article/view/v103i09</text>
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              <elementText elementTextId="88563">
                <text>Universidad de Castilla-La Mancha</text>
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            <elementTextContainer>
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                <text>Fajar bagus W</text>
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                <text>English</text>
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                  <text>VOLUME 103 Tahun 2022</text>
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                  <text>https://www.jstatsoft.org/issue/view/v103</text>
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                  <text>English</text>
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                <text>Hierarchical Clustering with Contiguity Constraint in R</text>
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                <text>R, hclust, constrained clustering, space, chronological clustering, Lance and Williams&#13;
algorithm</text>
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                <text>This article presents a new implementation of hierarchical clustering for the R language&#13;
that allows one to apply spatial or temporal contiguity constraints during the clustering&#13;
process. The need for contiguity constraint arises, for instance, when one wants to partition a map into different domains of similar physical conditions, identify discontinuities in&#13;
time series, group regional administrative units with respect to their performance, and so&#13;
on. To increase computation efficiency, we programmed the core functions in plain C. The&#13;
result is a new R function, constr.hclust, which is distributed in package adespatial.&#13;
The program implements the general agglomerative hierarchical clustering algorithm&#13;
described by Lance and Williams (1966; 1967), with the particularity of allowing only&#13;
clusters that are contiguous in geographic space or along time to fuse at any given step.&#13;
Contiguity can be defined with respect to space or time. Information about spatial contiguity is provided by a connection network among sites, with edges describing the links&#13;
between connected sites. Clustering with a temporal contiguity constraint is also known&#13;
as chronological clustering. Information on temporal contiguity can be implicitly provided&#13;
as the rank positions of observations in the time series. The implementation was mirrored&#13;
on that found in the hierarchical clustering function hclust of the standard R package&#13;
stats (R Core Team 2022). We transcribed that function from Fortran to C and added the&#13;
functionality to apply constraints when running the function.&#13;
The implementation is efficient. It is limited mainly by input/output access as massive&#13;
amounts of memory are potentially needed to store copies of the dissimilarity matrix and&#13;
update its elements when analyzing large problems. We provided R computer code for&#13;
plotting results for numbers of clusters.&#13;
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                <text>: data science, human mobility, mobility analysis, spatio-temporal analysis, big data,&#13;
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                <text>The last decade has witnessed the emergence of massive mobility datasets, such as&#13;
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individuals in a database. A view on state-of-the-art cannot avoid noticing that there is&#13;
no statistical software that can support scientists and practitioners with all the aspects&#13;
mentioned above of mobility data analysis. In this paper, we propose scikit-mobility, a&#13;
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research, analyze mobility data, and simulate human mobility habits. scikit-mobility is&#13;
efficient and easy to use as it extends pandas, a popular Python library for data analysis.&#13;
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trajectories to generating synthetic data, from analyzing statistical patterns to assessing&#13;
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for Unicode), should be included in each statistician’s or data scientist’s repertoire to&#13;
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              <elementText elementTextId="88491">
                <text>Text</text>
              </elementText>
            </elementTextContainer>
          </element>
        </elementContainer>
      </elementSet>
    </elementSetContainer>
  </item>
</itemContainer>
