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                <text>convex optimization, disciplined convex optimization, optimization, regression,&#13;
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                <text>CVXR is an R package that provides an object-oriented modeling language for convex&#13;
optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows the user to&#13;
formulate convex optimization problems in a natural mathematical syntax rather than&#13;
the restrictive form required by most solvers. The user specifies an objective and set of&#13;
constraints by combining constants, variables, and parameters using a library of functions with known mathematical properties. CVXR then applies signed disciplined convex&#13;
programming (DCP) to verify the problem’s convexity. Once verified, the problem is&#13;
converted into standard conic form using graph implementations and passed to a cone&#13;
solver such as ECOS or SCS. We demonstrate CVXR’s modeling framework with several&#13;
applications</text>
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                <text>Fajar bagus W</text>
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                <text>Empirical Bayes inference assumes an unknown prior density g(θ) has yielded (unobservables) Θ1, Θ2, . . . , ΘN , and each Θi produces an independent observation Xi from&#13;
pi(Xi&#13;
|Θi). The marginal density fi(Xi) is a convolution of the prior g and pi&#13;
. The Bayes&#13;
deconvolution problem is one of recovering g from the data. Although estimation of g – so&#13;
called g-modeling – is difficult, the results are more encouraging if the prior g is restricted&#13;
to lie within a parametric family of distributions. We present a deconvolution approach&#13;
where g is restricted to be in a parametric exponential family, along with an R package&#13;
deconvolveR designed for the purpose</text>
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                <text>Balasubramanian Narasimhan</text>
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                <text>Fajar bagus W</text>
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                <text>preference: An R Package for Two-Stage Clinical Trial Design Accounting for Patient Preference</text>
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                <text>two-stage clinical trials, preference, sample size.</text>
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                <text>The consideration of a patient’s treatment preference may be essential in determining&#13;
how a patient will respond to a particular treatment. While traditional clinical trials are&#13;
unable to capture these effects, the two-stage randomized preference design provides an&#13;
important tool for researchers seeking to understand the role of patient preferences. In&#13;
addition to the treatment effect, these designs seek to estimate the role of preferences&#13;
through testing of selection and preference effects. The R package preference facilitates&#13;
the use of two-stage clinical trials by providing the necessary tools to design and analyze&#13;
these studies. To aid in the design, functions are provided to estimate the required sample&#13;
size and to estimate the study power when a sample size is fixed. In addition, analysis&#13;
functions are provided to determine the significance of each effect using either raw data or&#13;
summary statistics. The package is able to incorporate either an unstratified or stratified&#13;
preference design. The functionality of the package is demonstrated using data from&#13;
a study evaluating two management methods in women found to have an atypical Pap&#13;
smear</text>
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                <text>Briana Cameron</text>
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                <text>https://www.jstatsoft.org/article/view/v094c02</text>
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                <text>23andMe</text>
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                <text>June 2020</text>
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                <text>Fajar bagus W</text>
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                <text>BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models</text>
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                <text>best subset selection, primal dual active set, model selection, variable selection, R,&#13;
C++, Rcpp</text>
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                <text>We introduce a new R package, BeSS, for solving the best subset selection problem&#13;
in linear, logistic and Cox’s proportional hazard (CoxPH) models. It utilizes a highly&#13;
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>Canhong Wen</text>
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                <text>https://www.jstatsoft.org/article/view/v094i04</text>
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                <text>University of Science&#13;
and Technology of China&#13;
Sun Yat-sen University</text>
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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;
study</text>
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                <text>We present the R package BASS as a tool for nonparametric regression. The primary&#13;
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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> maximum tolerated dose, dose finding, phase I trials, model-assisted design, Bayesian&#13;
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                <text>This article describes the R package BOIN, which implements a recently developed&#13;
methodology for designing single-agent and drug-combination dose-finding clinical trials&#13;
using Bayesian optimal interval designs (Liu and Yuan 2015; Yuan, Hess, Hilsenbeck,&#13;
and Gilbert 2016). The BOIN designs are novel “model-assisted” phase I trial designs&#13;
that can be implemented simply and transparently, similar to the 3 + 3 design, but yield&#13;
excellent performance comparable to those of more complicated, model-based designs.&#13;
The BOIN package provides tools for designing, conducting, and analyzing single-agent&#13;
and drug-combination dose-finding trials</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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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;
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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;
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