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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>The rscala software is a simple, two-way bridge between R and Scala that allows users&#13;
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be instantiated from R and Scala methods can be called. Arbitrary Scala code can be&#13;
executed on-the-fly from within R and callbacks to R are supported. R packages can be&#13;
developed based on Scala. Conversely, rscala also enables R code to be embedded within&#13;
a Scala application. The rscala package is available from the Comprehensive R Archive&#13;
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                <text> temporal networks, dynamic networks, preferential attachment, fitness, rich-getricher, fit-get-richer, R, C++</text>
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                <text>Many real-world systems are profitably described as complex networks that grow over&#13;
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not only explain certain structural properties commonly observed in real-world systems,&#13;
but are also tied to a number of applications in modeling and inference. While there are&#13;
statistical packages for estimating various parametric forms of the preferential attachment&#13;
function, there is no such package implementing non-parametric estimation procedures.&#13;
The non-parametric approach to the estimation of the preferential attachment function&#13;
allows for comparatively finer-grained investigations of the “rich-get-richer” phenomenon&#13;
that could lead to novel insights in the search to explain certain nonstandard structural&#13;
properties observed in real-world networks. This paper introduces the R package PAFit,&#13;
which implements non-parametric procedures for estimating the preferential attachment&#13;
function and node fitnesses in a growing network, as well as a number of functions for&#13;
generating complex networks from these two mechanisms. The main computational part&#13;
of the package is implemented in C++ with OpenMP to ensure scalability to large-scale&#13;
networks. In this paper, we first introduce the main functionalities of PAFit through&#13;
simulated examples, and then use the package to analyze a collaboration network between&#13;
scientists in the field of complex networks. The results indicate the joint presence of “richget-richer” and “fit-get-richer” phenomena in the collaboration network. The estimated&#13;
attachment function is observed to be near-linear, which we interpret as meaning that&#13;
the chance an author gets a new collaborator is proportional to their current number&#13;
of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar&#13;
faces from the complex networks community among the field’s topmost fittest network&#13;
scientist</text>
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                <text>RIKEN Center for AIP</text>
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                <text>Fajar bagus W</text>
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                <text>M-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find&#13;
roots and compute the empirical sandwich variance estimator for any set of user-specified,&#13;
unbiased estimating equations. Examples from the M-estimation primer by Stefanski and&#13;
Boos (2002) demonstrate use of the software. The package also includes a framework&#13;
for finite sample, heteroscedastic, and autocorrelation variance corrections, and a website&#13;
with an extensive collection of tutorials.</text>
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                <text>https://www.jstatsoft.org/article/view/v092i02</text>
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                  <text>VOLUME 92 Tahun 2020</text>
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                  <text>https://www.jstatsoft.org/issue/view/v092</text>
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                <text>Most Likely Transformations: The mlt Package</text>
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                <text>Transformation model, Transformation analysis</text>
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                <text>The mlt package implements maximum likelihood estimation in the class of conditional transformation models. Based on a suitable explicit parameterization of the unconditional or conditional transformation function using infrastructure from package basefun, we show how one can define, estimate, and compare a cascade of increasingly complex transformation models in the maximum likelihood framework. Models for the unconditional or conditional distribution function of any univariate response variable are set-up and estimated in the same computational framework simply by choosing an appropriate transformation function and parameterization thereof. As it is computationally cheap to evaluate the distribution function, models can be estimated by maximization of the exact likelihood, especially in the presence of random censoring or truncation. The relatively dense high-level implementation in the R system for statistical computing allows generalization of many established implementations of linear transformation models, such as the Cox model or other parametric models for the analysis of survival or ordered categorical data, to the more complex situations illustrated in this paper</text>
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                <text>Torsten Hothorn</text>
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                <text>https://www.jstatsoft.org/article/view/v092i01</text>
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                <text>Universität Zürich</text>
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                <text>Fajar bagus w</text>
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