Coxphmic: An R Package For Sparse Estimation Of Cox Proportional Hazards Models Via Approximated Information Criteria,
2017
Johns Hopkins University
Coxphmic: An R Package For Sparse Estimation Of Cox Proportional Hazards Models Via Approximated Information Criteria, Razieh Nabi, Xiaogang Su
The R Journal
In this paper, we describe an R package named coxphMIC, which implements the sparse estimation method for Cox proportional hazards models via approximated information criterion (Su et al., 2016). The developed methodology is named MIC which stands for “Minimizing approximated Information Criteria". A reparameterization step is introduced to enforce sparsity while at the same time keeping the objective function smooth. As a result, MIC is computationally fast with a superior performance in sparse estimation. Furthermore, the reparameterization tactic yields an additional advantage in terms of circumventing post-selection inference (Leeb and Pötscher, 2005). The MIC method and its R implementation …
Isogenegui: Multiple Approaches For Dose-Response Analysis Of Microarray Data Using R,
2017
Universiteit Hasselt
Isogenegui: Multiple Approaches For Dose-Response Analysis Of Microarray Data Using R, Martin Otava, Rudradev Sengupta, Ziv Shkedy, Dan Lin, Setia Pramana, Tobias Verbeke, Philippe Haldermans, Ludwig A. Hothorn, Daniel Gerhard, Rebecca M. Kuiper, Florian Klinglmueller, Adetayo Kasim
The R Journal
The analysis of transcriptomic experiments with ordered covariates, such as dose-response data, has become a central topic in bioinformatics, in particular in omics studies. Consequently, multiple R packages on CRAN and Bioconductor are designed to analyse microarray data from various perspectives under the assumption of order restriction. We introduce the new R package IsoGene Graphical User Interface (IsoGeneGUI), an extension of the original IsoGene package that includes methods from most of available R packages designed for the analysis of order restricted microarray data, namely orQA, ORIClust, goric and ORCME. The methods included in the new …
Gsympoint: An R Package To Estimate The Generalized Symmetry Point, An Optimal Cut-Off Point For Binary Classification In Continuous Diagnostic Tests,
2017
Universidad de Santiago de Compostela
Gsympoint: An R Package To Estimate The Generalized Symmetry Point, An Optimal Cut-Off Point For Binary Classification In Continuous Diagnostic Tests, Mónica López-Ratón, Elisa M. Molanes-López, Emilio Letón, Carmen Cadarso-Suárez
The R Journal
In clinical practice, it is very useful to select an optimal cutpoint in the scale of a continuous biomarker or diagnostic test for classifying individuals as healthy or diseased. Several methods for choosing optimal cutpoints have been presented in the literature, depending on the ultimate goal. One of these methods, the generalized symmetry point, recently introduced, generalizes the symmetry point by incorporating the misclassification costs. Two statistical approaches have been proposed in the literature for estimating this optimal cutpoint and its associated sensitivity and specificity measures, a parametric method based on the generalized pivotal quantity and a nonparametric method based …
News From The Bioconductor Project,
2017
University of Nebraska - Lincoln
News From The Bioconductor Project, Bioconductor Core Team
The R Journal
The Bioconductor project provides tools for the analysis and comprehension of high throughput genomic data. Bioconductor 3.5 was released on 25 April, 2017. It is compatible with R 3.4 and consists of 1383 software packages, 316 experiment data packages, and 911 up-to-date annotation packages. The release announcement includes descriptions of 88 new packages, and updated NEWS files for many additional packages. Start using Bioconductor by installing the most recent version of R and evaluating the commands
The Introduction Of Informal Cooperative Learning Into Our Programming Laboratories,
2017
Purdue University
The Introduction Of Informal Cooperative Learning Into Our Programming Laboratories, Guity Ravai, Ludmila Nunes, Ronald Erdei
IMPACT Presentations
Presented at the Women in Engineering ProActive Network (WEPAN) Change Leader Forum: Creating a Mindset for Action in Westminster, CO, USA
Bayesbinmix: An R Package For Model Based Clustering Of Multivariate Binary Data,
2017
University of Manchester
Bayesbinmix: An R Package For Model Based Clustering Of Multivariate Binary Data, Panagiotis Papastamoulis, Magnus Rattray
The R Journal
The BayesBinMix package offers a Bayesian framework for clustering binary data with or without missing values by fitting mixtures of multivariate Bernoulli distributions with an unknown number of components. It allows the joint estimation of the number of clusters and model parameters using Markov chain Monte Carlo sampling. Heated chains are run in parallel and accelerate the convergence to the target posterior distribution. Identifiability issues are addressed by implementing label switching algorithms. The package is demonstrated and benchmarked against the Expectation Maximization algorithm using a simulation study as well as a real dataset.
Bug Characteristics In Blockchain Systems: A Large-Scale Empirical Study,
2017
Singapore Management University
Bug Characteristics In Blockchain Systems: A Large-Scale Empirical Study, Zhiyuan Wan, David Lo, Xin Xia, Liang Cai
Research Collection School Of Computing and Information Systems
Bugs severely hurt blockchain system dependability. A thorough understanding of blockchain bug characteristics is required to design effective tools for preventing, detecting and mitigating bugs. We perform an empirical study on bug characteristics in eight representative open source blockchain systems. First, we manually examine 1,108 bug reports to understand the nature of the reported bugs. Second, we leverage card sorting to label the bug reports, and obtain ten bug categories in blockchain systems. We further investigate the frequency distribution of bug categories across projects and programming languages. Finally, we study the relationship between bug categories and bug fixing time. The …
Cataloging Github Repositories,
2017
Singapore Management University
Cataloging Github Repositories, Abhishek Sharma, Ferdian Thung, Pavneet Singh Kochhar, Agus Sulistya, David Lo
Research Collection School Of Computing and Information Systems
GitHub is one of the largest and most popular repository hosting service today, having about 14 million users and more than 54 million repositories as of March 2017. This makes it an excellent platform to find projects that developers are interested in exploring. GitHub showcases its most popular projects by cataloging them manually into categories such as DevOps tools, web application frameworks, and game engines. We propose that such cataloging should not be limited only to popular projects. We explore the possibility of developing such cataloging system by automatically extracting functionality descriptive text segments from readme files of GitHub repositories. …
Psf: Introduction To R Package For Pattern Sequence Based Forecasting Algorithm,
2017
Visvesvaraya National Institute of Technology
Psf: Introduction To R Package For Pattern Sequence Based Forecasting Algorithm, Neeraj Bokde, Gualberto Asencio-Cortés, Francisco Martínez-Álvarez, Kishore Kulat
The R Journal
This paper introduces the R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF …
Flan: An R Package For Inference On Mutation Models,
2017
Université Grenoble Alpes
Flan: An R Package For Inference On Mutation Models, Adrien Mazoyer, Rémy Drouilhet, Stéphane Despréaux, Bernard Ycart
The R Journal
This paper describes flan, a package providing tools for fluctuation analysis of mutant cell counts. It includes functions dedicated to the distribution of final numbers of mutant cells. Parametric estimation and hypothesis testing are also implemented, enabling inference on different sorts of data with several possible methods. An overview of the subject is proposed. The general form of mutation models is described, including the classical models as particular cases. Estimating from a model, when the data have been generated by another, induces different possible biases, which are identified and discussed. The three estimation methods available in the package are …
Market Area Analysis For Retail And Service Locations With Mci,
2017
Karlsruhe Institute of Technology
Market Area Analysis For Retail And Service Locations With Mci, Thomas Wieland
The R Journal
In retail location analysis, marketing research and spatial planning, the market areas of stores and/or locations are a frequent subject. Market area analyses consist of empirical observations and modeling via theoretical and/or econometric models such as the Huff Model or the Multiplicative Competitive Interaction Model. The authors’ package MCI implements the steps of market area analysis into R with a focus on fitting the models and data preparation and processing.
Pgee: An R Package For Analysis Of Longitudinal Data With High-Dimensional Covariates,
2017
Middle East Technical University
Pgee: An R Package For Analysis Of Longitudinal Data With High-Dimensional Covariates, Gul Inan, Lan Wang
The R Journal
We introduce an R package PGEE that implements the penalized generalized estimating equations (GEE) procedure proposed by Wang et al. (2012) to analyze longitudinal data with a large number of covariates. The PGEE package includes three main functions: CVfit, PGEE, and MGEE. The CVfit function computes the cross-validated tuning parameter for penalized generalized estimating equations. The function PGEE performs simultaneous estimation and variable selection for longitudinal data with high-dimensional covariates; whereas the function MGEE fits unpenalized GEE to the data for comparison. The R package PGEE is illustrated using a yeast cell-cycle gene expression data set.
Update Of The Nlme Package To Allow A Fixed Standard Deviation Of The Residual Error,
2017
BioStat-Plus B.V.
Update Of The Nlme Package To Allow A Fixed Standard Deviation Of The Residual Error, Simon H. Heisterkamp, Engelbertus Van Willigen, Paul-Matthias Diderichsen, John Maringwa
The R Journal
The use of linear and non-linear mixed models in the life sciences and pharmacometrics is common practice. Estimation of the parameters of models not involving a system of differential equations is often done by the R or S-Plus software with the nonlinear mixed effects nlme package. The estimated residual error may be used for diagnosis of the fitted model, but not whether the model correctly describes the relation between response and included variables including the true covariance structure. The latter is only true if the residual error is known in advance. Therefore, it maybe necessary or more appropriate to fix …
Mdplot: Visualise Molecular Dynamics,
2017
University of Natural Resources and Life Sciences
Mdplot: Visualise Molecular Dynamics, Christian Margreitter, Chris Oostenbrink
The R Journal
The MDplot package provides plotting functions to allow for automated visualisation of molecular dynamics simulation output. It is especially useful in cases where the plot generation is rather tedious due to complex file formats or when a large number of plots are generated. The graphs that are supported range from those which are standard, such as RMSD/RMSF (root-mean-square deviation and root-mean-square fluctuation, respectively) to less standard, such as thermodynamic integration analysis and hydrogen bond monitoring over time. All told, they address many commonly used analyses. In this article, we set out the MDplot package’s functions, give examples of the function …
Implementing A Metapopulation Bass Diffusion Model Using The R Package Desolve,
2017
National University of Ireland Galway
Implementing A Metapopulation Bass Diffusion Model Using The R Package Desolve, Jim Duggan
The R Journal
Diffusion is a fundamental process in physical, biological, social and economic settings. Consumer products often go viral, with sales driven by the word of mouth effect, as their adoption spreads through a population. The classic diffusion model used for product adoption is the Bass diffusion model, and this divides a population into two groups of people: potential adopters who are likely to adopt a product, and adopters who have purchased the product, and influence others to adopt. The Bass diffusion model is normally captured in an aggregate form, where no significant consumer differences are modeled. This paper extends the Bass …
Aliner: An R Package For Optimizing Feature-Weighted Alignments And Linguistic Distances,
2017
University of Maryland
Aliner: An R Package For Optimizing Feature-Weighted Alignments And Linguistic Distances, Sean S. Downey, Guowei Sun, Peter Norquest
The R Journal
Linguistic distance measurements are commonly used in anthropology and biology when quantitative and statistical comparisons between words are needed. This is common, for example, when analyzing linguistic and genetic data. Such comparisons can provide insight into historical population patterns and evolutionary processes. However, the most commonly used linguistic distances are derived from edit distances, which do not weight phonetic features that may, for example, represent smaller-scale patterns in linguistic evolution. Thus, computational methods for calculating feature-weighted linguistic distances are needed for linguistic, biological, and evolutionary applications; additionally, the linguistic distances presented here are generic and may have broader applications in …
Minval: An R Package For Minimal Validation Of Stoichiometric Reactions,
2017
Universidad Nacional de Colombia
Minval: An R Package For Minimal Validation Of Stoichiometric Reactions, Daniel Osorio, Janneth González, Andrés Pinzón
The R Journal
A genome-scale metabolic reconstruction is a compilation of all stoichiometric reactions that can describe the entire cellular metabolism of an organism, and they have become an indispensable tool for our understanding of biological phenomena, covering fields that range from systems biology to bioengineering. Interrogation of metabolic reconstructions are generally carried through Flux Balance Analysis, an optimization method in which the biological sense of the optimal solution is highly sensitive to thermodynamic unbalance caused by the presence of stoichiometric reactions whose compounds are not produced or consumed in any other reaction (orphan metabolites) and by mass unbalance. The minval package was …
Iotools: High-Performance I/O Tools For R,
2017
University of Richmond
Iotools: High-Performance I/O Tools For R, Taylor Arnold, Michael J. Kane, Simon Urbanek
The R Journal
The iotools package provides a set of tools for input and output intensive data processing in R. The functions chunk.apply and read.chunk are supplied to allow for iteratively loading contiguous blocks of data into memory as raw vectors. These raw vectors can then be efficiently converted into matrices and data frames with the iotools functions mstrsplit and dstrsplit. These functions minimize copying of data and avoid the use of intermediate strings in order to drastically improve performance. Finally, we also provide read.csv.raw to allow users to read an entire dataset into memory with the same efficient parsing code. In this …
Network Visualization With Ggplot2,
2017
Iowa State University
Network Visualization With Ggplot2, Sam Tyner, François Briatte, Heike Hofmann
The R Journal
This paper explores three different approaches to visualize networks by building on the grammar of graphics framework implemented in the ggplot2 package. The goal of each approach is to provide the user with the ability to apply the flexibility of ggplot2 to the visualization of network data, including through the mapping of network attributes to specific plot aesthetics. By incorporating networks in the ggplot2 framework, these approaches (1) allow users to enhance networks with additional information on edges and nodes, (2) give access to the strengths of ggplot2, such as layers and facets, and (3) convert network data objects …
Working With Daily Climate Model Output Data In R And The Futureheatwaves Package,
2017
Colorado State University
Working With Daily Climate Model Output Data In R And The Futureheatwaves Package, G Brooke Anderson, Colin Eason, Elizabeth A. Barnes
The R Journal
Research on climate change impacts can require extensive processing of climate model output, especially when using ensemble techniques to incorporate output from multiple climate models and multiple simulations of each model. This processing can be particularly extensive when identifying and characterizing multi-day extreme events like heat waves and frost day spells, as these must be processed from model output with daily time steps. Further, climate model output is in a format and follows standards that may be unfamiliar to most R users. Here, we provide an overview of working with daily climate model output data in R. We then present …
