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                <text>: space-time covariance functions, symmetry, separability, type of non-separability,&#13;
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                <text>Although a very rich list of classes of space-time covariance functions exists, specific&#13;
tools for selecting the appropriate class for a given data set are needed. Thus, the main&#13;
topic of this paper is to present the new R package, covatest, which can be used for&#13;
testing some characteristics of a covariance function, such as symmetry, separability and&#13;
type of non-separability, as well as for testing the adequacy of some classes of space-time&#13;
covariance models. These last aspects can be relevant for choosing a suitable class of&#13;
covariance models. The proposed results have been applied to an environmental case&#13;
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                <text> Bayesian design of experiments, screening experiments, Bayesian model selection,&#13;
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                <text>Fractional factorial experiments often produce ambiguous results due to confounding&#13;
among the factors; as a consequence more than one model is consistent with the data.&#13;
Thus, the practical problem is how to choose additional runs in order to discriminate&#13;
among the rival models and to identify the active factors. The R package OBsMD solves&#13;
this problem by implementing the objective Bayesian methodology proposed by Consonni&#13;
and Deldossi (2016). The main feature of this approach is that the follow-up designs are&#13;
obtained through the use of just two functions, OBsProb() and OMD() without requiring&#13;
any prior specifications, being fully automatic. Thus OBsMD provides a simple tool for&#13;
conducting a design of experiments to solve real world problems.</text>
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                <text>Universitá Cattolica del Sacro Cuore</text>
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                <text> Bayesian network, belief propagation, R, shiny, gene networks, visualization</text>
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                <text>The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed&#13;
(discrete and continuous). Probabilistic reasoning enables a user to absorb information&#13;
into a Bayesian network and make queries about how the probabilities within the network&#13;
change in light of new information. The package was developed in the R programming&#13;
language and is freely available from the Comprehensive R Archive Network. A shiny app&#13;
with Cytoscape widgets provides an interactive interface for evidence absorption, queries,&#13;
and visualizations</text>
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                <text>Roswell Park Comprehensive&#13;
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                <text>We introduce a new R package, BeSS, for solving the best subset selection problem&#13;
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efficient active set algorithm based on primal and dual variables, and supports sequential&#13;
and golden search strategies for best subset selection. We provide a C++ implementation&#13;
of the algorithm using an Rcpp interface. We demonstrate through numerical experiments based on enormous simulation and real datasets that the new BeSS package has&#13;
competitive performance compared to other R packages for best subset selection purposes</text>
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                <text>University of Science&#13;
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Sun Yat-sen University</text>
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                <text>The R package AssocTests provides some procedures which are commonly used in&#13;
genetic association studies. These procedures are population stratification correction&#13;
through eigenvectors, principal coordinates of clusterings, Tracy-Widom test, distance&#13;
regression, single-marker test, maximum test based on three Cochran-Armitage trend&#13;
tests, non-parametric trend test, and non-parametric maximum test. The trait values for&#13;
these methods should be discrete or continuous. The discrete traits can be coded by 1/0&#13;
for cases/controls. The genotype values can be 0, 1, or 2 indicating the number of risk&#13;
alleles for a biallelic single-nucleotide polymorphism. This article introduces the methods&#13;
and algorithms implemented in the package. Some examples are provided to illustrate&#13;
the package’s capability</text>
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Economics and Business</text>
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                <text>Disease spreading simulations are traditionally performed using coupled differential&#13;
equations. However, in the setting of metapopulations, most of the solutions provided by&#13;
this method do not account for the dynamic topography of subpopulations. Conversely,&#13;
the alternative approach of individual-based modeling (IBM) may add computational cost&#13;
and complexity.&#13;
Hybrid models allow for the study of disease spreading because they combine both&#13;
aforementioned approaches by separating them across different scales: a local scale that&#13;
addresses subpopulation dynamics using coupled differential equations and a global scale&#13;
that addresses the contact between these subpopulations using IBM.&#13;
We present a simple way of simulating the spread of disease in dynamic networks using the high-level statistical computational language R and the hybridModels package.&#13;
We built four examples using disease spread models at the local scale in several different&#13;
networks: an animal movement network; a three-node network, whose model solution&#13;
using a stochastic simulation algorithm is compared with the ordinary differential equations approach; the commuting of individuals between patches, which we compare with&#13;
the permanent migration of individuals; and the commuting of individuals within the&#13;
metropolitan area of São Paulo</text>
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                <text>: credible subgroups, multiple hypothesis testing, R, subset identification, subgroup&#13;
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                <text>Subset identification methods are used to select the subset of a covariate space over&#13;
which the conditional distribution of a response has certain properties – for example,&#13;
identifying types of patients whose conditional treatment effect is positive. An often&#13;
critical requirement of subset identification methods is multiplicity control, by which the&#13;
family-wise Type I error rate is controlled, rather than the Type I error rate of each&#13;
covariate-determined hypothesis separately. The credible subset (or credible subgroup)&#13;
method provides a multiplicity-controlled estimate of the target subset in the form of two&#13;
bounding subsets: one which entirely contains the target subset, and one which is entirely&#13;
contained by it.&#13;
We introduce a new R package, credsubs, which constructs credible subset estimates&#13;
using a sample from the joint posterior distribution of any regression model, a description&#13;
of the covariate space, and a function mapping the parameters and covariates to the subset&#13;
criterion. We demonstrate parametric and nonparametric applications of the package to&#13;
a clinical trial dataset and a neuroimaging dataset, respectively.</text>
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                <text>Fajar Bagus W</text>
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                <text>BASS: An R Package for Fitting and Performing Sensitivity Analysis of Bayesian Adaptive Spline Surfaces</text>
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                <text> splines, functional data analysis, sensitivity analysis, nonparametric regression.</text>
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                <text>We present the R package BASS as a tool for nonparametric regression. The primary&#13;
focus of the package is fitting fully Bayesian adaptive spline surface (BASS) models and&#13;
performing global sensitivity analyses of these models. The BASS framework is similar to&#13;
that of Bayesian multivariate adaptive regression splines (BMARS) from Denison, Mallick,&#13;
and Smith (1998), but with many added features. The software is built to efficiently&#13;
handle significant amounts of data with many continuous or categorical predictors and&#13;
with functional response. Under our Bayesian framework, most priors are automatic but&#13;
these can be modified by the user to focus on parsimony and the avoidance of overfitting.&#13;
If directed to do so, the software uses parallel tempering to improve the reversible jump&#13;
Markov chain Monte Carlo (RJMCMC) methods used to perform inference. We discuss&#13;
the implementation of these features and present the performance of BASS in a number&#13;
of analyses of simulated and real data.</text>
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                <text>A large number of statistical decision problems in the social sciences and beyond can&#13;
be framed as a (contextual) multi-armed bandit problem. However, it is notoriously hard&#13;
to develop and evaluate policies that tackle these types of problems, and to use such&#13;
policies in applied studies. To address this issue, this paper introduces StreamingBandit, a Python web application for developing and testing bandit policies in field studies.&#13;
StreamingBandit can sequentially select treatments using (online) policies in real time.&#13;
Once StreamingBandit is implemented in an applied context, different policies can be&#13;
tested, altered, nested, and compared. StreamingBandit makes it easy to apply a multitude of bandit policies for sequential allocation in field experiments, and allows for the&#13;
quick development and re-use of novel policies. In this article, we detail the implementation logic of StreamingBandit and provide several examples of its use</text>
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                <text>: functional data analysis, function-on-function regression, function-on-scalar regression, gradient boosting, model-based boosting, scalar-on-function regression.</text>
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                <text>The R add-on package FDboost is a flexible toolbox for the estimation of functional&#13;
regression models by model-based boosting. It provides the possibility to fit regression&#13;
models for scalar and functional response with effects of scalar as well as functional covariates, i.e., scalar-on-function, function-on-scalar and function-on-function regression&#13;
models. In addition to mean regression, quantile regression models as well as generalized&#13;
additive models for location scale and shape can be fitted with FDboost. Furthermore,&#13;
boosting can be used in high-dimensional data settings with more covariates than observations. We provide a hands-on tutorial on model fitting and tuning, including the&#13;
visualization of results. The methods for scalar-on-function regression are illustrated&#13;
with spectrometric data of fossil fuels and those for functional response regression with a&#13;
data set including bioelectrical signals for emotional episodes</text>
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                <text>Fajar bagus W</text>
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