Bayesspsurv: An R Package To Estimate Bayesian (Spatial) Split-Population Survival Models,
2021
Penn State University
Bayesspsurv: An R Package To Estimate Bayesian (Spatial) Split-Population Survival Models, Brandon Bolte, Nicolás Schmidt, Sergio Béjar, Nguyen Huynh, Bumba Mukherjee
The R Journal
Survival data often include a fraction of units that are susceptible to an event of interest as well as a fraction of “immune” units. In many applications, spatial clustering in unobserved risk factors across nearby units can also affect their survival rates and odds of becoming immune. To address these methodological challenges, this article introduces our BayesSPsurv R-package, which fits parametric Bayesian Spatial split-population survival (cure) models that can account for spatial autocorrelation in both subpopulations of the user’s time-to-event data. Spatial autocorrelation is modeled with spatially weighted frailties, which are estimated using a conditionally autoregressive prior. The user can …
Gofcopula: Goodness-Of-Fit Tests For Copulae,
2021
Technische Universität Dresden
Gofcopula: Goodness-Of-Fit Tests For Copulae, Ostap Okhrin, Simon Trimborn, Martin Waltz
The R Journal
The last decades show an increased interest in modeling various types of data through copulae. Different copula models have been developed, which lead to the challenge of finding the best fitting model for a particular dataset. From the other side, a strand of literature developed a list of different Goodness-of-Fit (GoF) tests with different powers under different conditions. The usual practice is the selection of the best copula via the p-value of the GoF test. Although this method is not purely correct due to the fact that non-rejection does not imply acception, this strategy is favored by practitioners. Unfortunately, …
Working With Crsp/Compustat In R: Reproducible Empirical Asset Pricing,
2021
Stevens Institute of Technology
Working With Crsp/Compustat In R: Reproducible Empirical Asset Pricing, Majeed Simaan
The R Journal
It is common to come across SAS or Stata manuals while working on academic empirical finance research. Nonetheless, given the popularity of open-source programming languages such as R, there are fewer resources in R covering popular databases such as CRSP and COMPUSTAT. The aim of this article is to bridge the gap and illustrate how to leverage R in working with both datasets. As an application, we illustrate how to form size-value portfolios with respect to Fama and French (1993) and study the sensitivity of the results with respect to different inputs. Ultimately, the purpose of the article is to …
Garchx: Flexible And Robust Garch-X Modeling,
2021
BI Norwegian Business School
Garchx: Flexible And Robust Garch-X Modeling, Genaro Sucarrat
The R Journal
The garchx package provides a user-friendly, fast, flexible, and robust framework for the estimation and inference of GARCH(p, q,r)-X models, where p is the ARCH order, q is the GARCH order, r is the asymmetry or leverage order, and ’X’ indicates that covariates can be included. Quasi Maximum Likelihood (QML) methods ensure estimates are consistent and standard errors valid, even when the standardized innovations are non-normal or dependent, or both. Zero-coefficient restrictions by omission enable parsimonious specifications, and functions to facilitate the non-standard inference associated with zero-restrictions in the null-hypothesis are provided. Finally, in the formal comparisons of …
Onestep: Le Cam's One-Step Estimation Procedure,
2021
Le Mans Université
Onestep: Le Cam's One-Step Estimation Procedure, Alexandre Brouste, Christophe Dutang, Darel Noutsa Mieniedou
The R Journal
The OneStep package proposes principally an eponymic function that numerically computes Le Cam’s one-step estimator, which is asymptotically efficient and can be computed faster than the maximum likelihood estimator for large datasets. Monte Carlo simulations are carried out for several examples (discrete and continuous probability distributions) in order to exhibit the performance of Le Cam’s one-step estimation procedure in terms of efficiency and computational cost on observation samples of finite size.
Wide-To-Tall Data Reshaping Using Regular Expressions And The Nc Package,
2021
Northern Arizona University
Wide-To-Tall Data Reshaping Using Regular Expressions And The Nc Package, Toby Dylan Hocking
The R Journal
Regular expressions are powerful tools for extracting tables from non-tabular text data. Capturing regular expressions that describe the information to extract from column names can be especially useful when reshaping a data table from wide (few rows with many regularly named columns) to tall (fewer columns with more rows). We present the R package nc (short for named capture), which provides functions for wide-to-tall data reshaping using regular expressions. We describe the main new ideas of nc, and provide detailed comparisons with related R packages (stats, utils, data.table, tidyr, tidyfast, tidyfst, reshape2, cdata).
Stratamatch: Prognostic Score Stratification Using A Pilot Design,
2021
Stanford University
Stratamatch: Prognostic Score Stratification Using A Pilot Design, Rachael C. Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Andrew B. Goldstone, Peter Chiu, Y Joseph Woo, Jonathan H. Chen
The R Journal
Optimal propensity score matching has emerged as one of the most ubiquitous approaches for causal inference studies on observational data. However, outstanding critiques of the statistical properties of propensity score matching have cast doubt on the statistical efficiency of this technique, and the poor scalability of optimal matching to large data sets makes this approach inconvenient if not infeasible for sample sizes that are increasingly commonplace in modern observational data. The stratamatch package provides implementation support and diagnostics for ‘stratified matching designs,’ an approach that addresses both of these issues with optimal propensity score matching for large-sample observational studies. First, …
Conversations In Time: Interactive Visualization To Explore Structured Temporal Data,
2021
The University of Auckland
Conversations In Time: Interactive Visualization To Explore Structured Temporal Data, Earo Wang, Dianne Cook
The R Journal
Temporal data often has a hierarchical structure, defined by categorical variables describing different levels, such as political regions or sales products. The nesting of categorical variables produces a hierarchical structure. The tsibbletalk package is developed to allow a user to interactively explore temporal data, relative to the nested or crossed structures. It can help to discover differences between category levels, and uncover interesting periodic or aperiodic slices. The package implements a shared tsibble object that allows for linked brushing between coordinated views, and a shiny module that aids in wrapping timelines for seasonal patterns. The tools are demonstrated using two …
Automating Reproducible, Collaborative Clinical Trial Document Generation With The Listdown Package,
2021
Yale University
Automating Reproducible, Collaborative Clinical Trial Document Generation With The Listdown Package, Michael Kane, Xun Jiang, Simon Urbanek
The R Journal
the conveyance of clinical trial explorations and analysis results from a statistician to a clinical investigator is a critical component of the drug development and clinical research cycle. Automating the process of generating documents for data descriptions, summaries, exploration, and analysis allows the statistician to provide a more comprehensive view of the information captured by a clinical trial, and efficient generation of these documents allows the statistican to focus more on the conceptual development of a trial or trial analysis and less on the implementation of the summaries and results on which decisions are made. This paper explores the use …
Editorial,
2021
Monash University
Editorial, Dianne Cook
The R Journal
First, some news about the journal board. Welcome to Gavin Simpson, who joins as a new Executive Editor! In addition, welcome to our new Associate Editors Nicholas Tierney, Isabella Gollini, Rasmus Bååth, Mark van der Loo, Elizabeth Sweeney, Louis Aslett and Katarina Domijan. With the large volume of submissions, the Associate Editors now play a vital role in processing articles.
Penphcure: Variable Selection In Proportional Hazards Cure Model With Time-Varying Covariates,
2021
HEC Liège
Penphcure: Variable Selection In Proportional Hazards Cure Model With Time-Varying Covariates, Alessandro Beretta, Cédric Heuchenne
The R Journal
We describe the penPHcure R package, which implements the semiparametric proportional-hazards (PH) cure model of Sy and Taylor (2000) extended to time-varying covariates and the variable selection technique based on its SCAD-penalized likelihood proposed by Beretta and Heuchenne (2019a). In survival analysis, cure models are a useful tool when a fraction of the population is likely to be immune from the event of interest. They can separate the effects of certain factors on the probability of being susceptible and on the time until the occurrence of the event. Moreover, the penPHcure package allows the user to simulate data from a …
Proving Non-Termination By Program Reversal,
2021
Singapore Management University
Proving Non-Termination By Program Reversal, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic
Research Collection School Of Computing and Information Systems
We present a new approach to proving non-termination of non-deterministic integer programs. Our technique is rather simple but efficient. It relies on a purely syntactic reversal of the program's transition system followed by a constraint-based invariant synthesis with constraints coming from both the original and the reversed transition system. The latter task is performed by a simple call to an off-the-shelf SMT-solver, which allows us to leverage the latest advances in SMT-solving. Moreover, our method offers a combination of features not present (as a whole) in previous approaches: it handles programs with non-determinism, provides relative completeness guarantees and supports programs …
Distr6: R6 Object-Oriented Probability Distributions Interface In R,
2021
University College London
Distr6: R6 Object-Oriented Probability Distributions Interface In R, Raphael Sonabend, Franz J. Király
The R Journal
distr6 is an object-oriented (OO) probability distributions interface leveraging the extensibility and scalability of R6 and the speed and efficiency of Rcpp. Over 50 probability distributions are currently implemented in the package with ‘core’ methods, including density, distribution, and generating functions, and more ‘exotic’ ones, including hazards and distribution function anti-derivatives. In addition to simple distributions, distr6 supports compositions such as truncation, mixtures, and product distributions. This paper presents the core functionality of the package and demonstrates examples for key use-cases. In addition, this paper provides a critical review of the object-oriented programming paradigms in R and describes some …
The R Package Smicd: Statistical Methods For Interval-Censored Data,
2021
Freie Universität Berlin
The R Package Smicd: Statistical Methods For Interval-Censored Data, Paul Walter
The R Journal
The package allows the use of two new statistical methods for the analysis of intervalcensored data: 1) direct estimation/prediction of statistical indicators and 2) linear (mixed) regression analysis. Direct estimation of statistical indicators, for instance, poverty and inequality indicators, is facilitated by a non parametric kernel density algorithm. The algorithm is able to account for weights in the estimation of statistical indicators. The standard errors of the statistical indicators are estimated with a non parametric bootstrap. Furthermore, the package offers statistical methods for the estimation of linear and linear mixed regression models with an interval-censored dependent variable, particularly random slope …
Finding Optimal Normalizing Transformations Via Bestnormalize,
2021
University of Colorado Anschutz Medical Campus
Finding Optimal Normalizing Transformations Via Bestnormalize, Ryan A. Peterson
The R Journal
The bestNormalize R package was designed to help users find a transformation that can effectively normalize a vector regardless of its actual distribution. Each of the many normalization techniques that have been developed has its own strengths and weaknesses, and deciding which to use until data are fully observed is difficult or impossible. This package facilitates choosing between a range of possible transformations and will automatically return the best one, i.e., the one that makes data look the most normal. To evaluate and compare the normalization efficacy across a suite of possible transformations, we developed a statistic based on a …
Dchaos: An R Package For Chaotic Time Series Analysis,
2021
Complutense University of Madrid
Dchaos: An R Package For Chaotic Time Series Analysis, Julio E. Sandubete, Lorenzo Escot
The R Journal
Chaos theory has been hailed as a revolution of thoughts and attracting ever-increasing attention of many scientists from diverse disciplines. Chaotic systems are non-linear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory is the detection of chaotic behavior from empirical time-series data. One of the main features of chaos is the well-known initial-value sensitivity property. Methods and techniques related to testing the hypothesis of chaos try to quantify the initial-value sensitive property estimating the so-called Lyapunov exponents. This paper describes the main estimation methods of the Lyapunov exponent from …
Benchmarking R Packages For Calculation Of Persistent Homology,
2021
Case Western Reserve University
Benchmarking R Packages For Calculation Of Persistent Homology, Eashwar V. Somasundaram, Shael E. Brown, Adam Litzler, Jacob G. Scott, Raoul R. Wadhwa
The R Journal
Several persistent homology software libraries have been implemented in R. Specifically, the Dionysus, GUDHI, and Ripser libraries have been wrapped by the TDA and TDAstats CRAN packages. These software represent powerful analysis tools that are computationally expensive and, to our knowledge, have not been formally benchmarked. Here, we analyze runtime and memory growth for the 2 R packages and the 3 underlying libraries. We find that datasets with less than 3 dimensions can be evaluated with persistent homology fastest by the GUDHI library in the TDA package. For higher-dimensional datasets, the Ripser library in the TDAstats package is the fastest. …
Unidimensional And Multidimensional Methods For Recurrence Quantification Analysis With Crqa,
2021
University of East London
Unidimensional And Multidimensional Methods For Recurrence Quantification Analysis With Crqa, Moreno I. Coco, Dan Mønster, Giuseppe Leonardi, Rick Dale, Sebastian Wallot
The R Journal
Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence-based analyses to quantify the dynamical structure of single and multivariate time series and capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto-recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version of our crqa 2.0 package. This package …
Exprior: An R Package For The Formulation Of Ex-Situ Priors,
2021
University Potsdam
Exprior: An R Package For The Formulation Of Ex-Situ Priors, Falk Heße, Karina Cucchi, Nura Kawa, Yoram Rubin
The R Journal
The exPrior package implements a procedure for formulating informative priors of geostatistical properties for a target field site, called ex-situ priors and introduced in Cucchi et al. (2019). The procedure uses a Bayesian hierarchical model to assimilate multiple types of data coming from multiple sites considered as similar to the target site. This prior summarizes the information contained in the data in the form of a probability density function that can be used to better inform further geostatistical investigations at the site. The formulation of the prior uses ex-situ data, where the data set can either be gathered by the …
A Method For Deriving Information From Running R Code,
2021
Statistics Netherlands
A Method For Deriving Information From Running R Code, Mark P. J. Van Der Loo
The R Journal
It is often useful to tap information from a running R script. Obvious use cases include monitoring the consumption of resources (time, memory) and logging. Perhaps less obvious cases include tracking changes in R objects or collecting the output of unit tests. In this paper, we demonstrate an approach that abstracts the collection and processing of such secondary information from the running R script. Our approach is based on a combination of three elements. The first element is to build a customized way to evaluate code. The second is labeled local masking and it involves temporarily masking a user-facing function …
