| 1. |
- Andersson, Björn, 1984-, et al.
(författare)
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Performing the Kernel Method of Test Equating with the Package kequate
- 2013
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Ingår i: Journal of Statistical Software. - : The American Statistical Association. - 1548-7660. ; 55:6, s. 1-25
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Tidskriftsartikel (refereegranskat)abstract
- In standardized testing it is important to equate tests in order to ensure that the test takers, regardless of the test version given, obtain a fair test. Recently, the kernel method of test equating, which is a conjoint framework of test equating, has gained popularity. The kernel method of test equating includes five steps: (1) pre-smoothing, (2) estimation of the score probabilities, (3) continuization, (4) equating, and (5) computing the standard error of equating and the standard error of equating difference. Here, an implementation has been made for six different equating designs: equivalent groups, single group, counter balanced, non-equivalent groups with anchor test using either chain equating or post- stratification equating, and non-equivalent groups using covariates. An R package for the kernel method of test equating called kequate is presented. Included in the package are also diagnostic tools aiding in the search for a proper log-linear model in the pre-smoothing step for use in conjunction with the R function glm.
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| 2. |
- Bolin, David, 1983, et al.
(författare)
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Calculating Probabilistic Excursion Sets and Related Quantities Using excursions
- 2018
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 86:5, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- The R software package excursions contains methods for calculating probabilistic excursion sets, contour credible regions, and simultaneous confidence bands for latent Gaussian stochastic processes and fields. It also contains methods for uncertainty quantification of contour maps and computation of Gaussian integrals. This article describes the theoretical and computational methods used in the package. The main functions of the package are introduced and two examples illustrate how the package can be used.
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| 3. |
- Bäcklin, Christofer L., 1983-, et al.
(författare)
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Developer-Friendly and Computationally Efficient Predictive Modeling without Information Leakage : The emil Package for R
- 2018
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Ingår i: Journal of Statistical Software. - : JOURNAL STATISTICAL SOFTWARE. - 1548-7660. ; 85:13, s. 1-30
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Tidskriftsartikel (refereegranskat)abstract
- Data driven machine learning for predictive modeling problems (classification, regression, or survival analysis) typically involves a number of steps beginning with data preprocessing and ending with performance evaluation. A large number of packages providing tools for the individual steps are available for R, but there is a lack of tools for facilitating rigorous performance evaluation of the complete procedures assembled from them by means of cross-validation, bootstrap, or similar methods. Such a tool should strictly prevent test set observations from influencing model training and meta- parameter tuning, so- called information leakage, in order to not produce overly optimistic performance estimates. Here we present a new package for R denoted emil (evaluation of modeling without information leakage) that offers this form of performance evaluation. It provides a transparent and highly customizable framework for facilitating the assembly, execution, performance evaluation, and interpretation of complete procedures for classification, regression, and survival analysis. The components of package emil have been designed to be as modular and general as possible to allow users to combine, replace, and extend them if needed. Package emil was also developed with scalability in mind and has a small computational overhead, which is a key requirement for analyzing the very big data sets now available in fields like medicine, physics, and finance. First package emil's functionality and usage is explained. Then three specific application examples are presented to show its potential in terms of parallelization, customization for survival analysis, and development of ensemble models. Finally a brief comparison to similar software is provided.
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| 4. |
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| 5. |
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| 6. |
- Dahlin, Johan, 1986-, et al.
(författare)
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Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models
- 2019
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Ingår i: Journal of Statistical Software. - Alexandria, VA, United States : American Statistical Association. - 1548-7660. ; 88:CN2, s. 1-41
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Tidskriftsartikel (refereegranskat)abstract
- This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial in the CRAN repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.
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| 7. |
- Edlund, Ove
(författare)
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CMregr - A Matlab software package for finding CM-Estimates for Regression
- 2004
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 10:3, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes how to use the Matlab software package CMregr, and also gives some limited information on the CM-estimation problem itself. For detailed information on the algorithms used in CMregr as well as extensive testings, please refer to Arslan, Edlund & Ekblom (2002) and Edlund & Ekblom (2004).
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| 8. |
- Helske, Jouni
(författare)
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KFAS : Exponential Family State Space Models in R
- 2017
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 78:10
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Tidskriftsartikel (refereegranskat)abstract
- State space modeling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes the R package KFAS for state space modeling with the observations from an exponential family, namely Gaussian, Poisson, binomial, negative binomial and gamma distributions. After introducing the basic theory behind Gaussian and non-Gaussian state space models, an illustrative example of Poisson time series forecasting is provided. Finally, a comparison to alternative R packages suitable for non-Gaussian time series modeling is presented.
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| 9. |
- Helske, Satu, 1985-, et al.
(författare)
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Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
- 2019
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Ingår i: Journal of Statistical Software. - Alexandria, VA, United States : American Statistical Association. - 1548-7660. ; 88:3, s. 1-32
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Tidskriftsartikel (refereegranskat)abstract
- Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariates. The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs. Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization of the HMM. Good graphical presentations of data and models are useful during the whole analysis process from the first glimpse at the data to model fitting and presentation of results. The package provides easy options for plotting parallel sequence data, and proposes visualizing HMMs as directed graphs.less thanbr /greater thanComment: 33 pages, 8 figures
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| 10. |
- Häggström, Jenny, et al.
(författare)
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CovSel : An R Package for Covariate Selection When Estimating Average Causal Effects
- 2015
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 68:1, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- We describe the R package CovSel, which reduces the dimension of the covariate vector for the purpose of estimating an average causal effect under the unconfoundedness assumption. Covariate selection algorithms developed in De Luna, Waernbaum, and Richardson (2011) are implemented using model-free backward elimination. We show how to use the package to select minimal sets of covariates. The package can be used with continuous and discrete covariates and the user can choose between marginal co-ordinate hypothesis tests and kernel-based smoothing as model-free dimension reduction techniques.
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| 11. |
- Karlsson, Andreas
(författare)
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Scientific WorkPlace 5.5 and LyX 1.4.2
- 2007
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistics. - 1548-7660. ; 17
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Recension (övrigt vetenskapligt/konstnärligt)
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| 12. |
- Karlsson, Maria, et al.
(författare)
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truncSP : an R package for estimation of semi-parametric truncated linear regression models
- 2014
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Ingår i: Journal of Statistical Software. - : American Statistical Association. - 1548-7660. ; 57:14, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and finite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.
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| 13. |
- Löfstedt, Tommy, et al.
(författare)
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Simulated Data for Linear Regression with Structured and Sparse Penalties: Introducing pylearn-simulate
- 2018
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistics. - 1548-7660. ; 87:3
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Tidskriftsartikel (refereegranskat)abstract
- A currently very active field of research is how to incorporate structure and prior knowledge in machine learning methods. It has lead to numerous developments in the field of non-smooth convex minimization. With recently developed methods it is possible to perform an analysis in which the computed model can be linked to a given structure of the data and simultaneously do variable selection to find a few important features in the data. However, there is still no way to unambiguously simulate data to test proposed algorithms, since the exact solutions to such problems are unknown.The main aim of this paper is to present a theoretical framework for generating simulated data. These simulated data are appropriate when comparing optimization algorithms in the context of linear regression problems with sparse and structured penalties. Additionally, this approach allows the user to control the signal-to-noise ratio, the correlation structure of the data and the optimization problem to which they are the solution.The traditional approach is to simulate random data without taking into account the actual model that will be fit to the data. But when using such an approach it is not possible to know the exact solution of the underlying optimization problem. With our contribution, it is possible to know the exact theoretical solution of a penalized linear regression problem, and it is thus possible to compare algorithms without the need to use, e.g., cross-validation.We also present our implementation, the Python package pylearn-simulate, available at https://github.com/neurospin/pylearn-simulate and released under the BSD 3clause license. We describe the package and give examples at the end of the paper.
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| 14. |
- Magnani, Matteo, et al.
(författare)
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Analysis of Multiplex Social Networks with R
- 2021
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 98:8, s. 1-30
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Tidskriftsartikel (refereegranskat)abstract
- Multiplex social networks are characterized by a common set of actors connected through multiple types of relations. The multinet package provides a set of R functions to analyze multiplex social networks within the more general framework of multilayer networks, where each type of relation is represented as a layer in the network. The package contains functions to import/export, create and manipulate multilayer networks, implementations of several state-of-the-art multiplex network analysis algorithms, e.g., for centrality measures, layer comparison, community detection and visualization. Internally, the package is mainly written in native C++ and integrated with R using the Rcpp package.
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| 15. |
- Meyer, Sebastian, et al.
(författare)
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Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance
- 2017
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 77:11
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Tidskriftsartikel (refereegranskat)abstract
- The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The open source R package surveillance can handle various levels of aggregation at which infective events have been recorded: individual-level time-stamped geo-referenced data (case reports) in either continuous space or discrete space, as well as counts aggregated by period and region. For each of these data types, the surveillance package implements tools for visualization, likelihoood inference and simulation from recently developed statistical regression frameworks capturing endemic and epidemic dynamics. Altogether, this paper is a guide to the spatio-temporal modeling of epidemic phenomena, exemplified by analyses of public health surveillance data on measles and invasive meningococcal disease.
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| 16. |
- Nordh, Jerker
(författare)
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pyParticleest : A Python framework for particle-based estimation methods
- 2017
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 78
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Tidskriftsartikel (refereegranskat)abstract
- Particle methods such as the particle filter and particle smoothers have proven very useful for solving challenging nonlinear estimation problems in a wide variety of fields during the last decade. However, there are still very few existing tools available to support and assist researchers and engineers in applying the vast number of methods in this field to their own problems. This paper identifies the common operations between the methods and describes a software framework utilizing this information to provide a flexible and extensible foundation which can be used to solve a large variety of problems in this domain, thereby allowing code reuse to reduce the implementation burden and lowering the barrier of entry for applying this exciting field of methods. The software implementation presented in this paper is freely available and permissively licensed under the GNU Lesser General Public License, and runs on a large number of hardware and software platforms, making it usable for a large variety of scenarios.
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| 17. |
- Rosenblad, Andreas, fil. dr, docent, 1973-
(författare)
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gretl 1.7.3
- 2008
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 25:1, s. 1-14
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Recension (övrigt vetenskapligt/konstnärligt)
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| 18. |
- Ryeznik, Yevgen, et al.
(författare)
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RARtool : A MATLAB Software Package for Designing Response-Adaptive Randomized Clinical Trials with Time-to-Event Outcomes
- 2015
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Ingår i: Journal of Statistical Software. - 1548-7660. ; 66:1
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Tidskriftsartikel (refereegranskat)abstract
- Response-adaptive randomization designs are becoming increasingly popular in clinical trial practice. In this paper, we present RARtool, a user interface software developed in MATLAB for designing response-adaptive randomized comparative clinical trials with censored time-to-event outcomes. The RARtool software can compute different types of optimal treatment allocation designs, and it can simulate response-adaptive randomization procedures targeting selected optimal allocations. Through simulations, an investigator can assess design characteristics under a variety of experimental scenarios and select the best procedure for practical implementation. We illustrate the utility of our RARtool software by redesigning a survival trial from the literature.
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| 19. |
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| 20. |
- Sachs, MC
(författare)
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plotROC: A Tool for Plotting ROC Curves
- 2017
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Ingår i: Journal of statistical software. - : Foundation for Open Access Statistic. - 1548-7660. ; 79:CN2
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Tidskriftsartikel (refereegranskat)
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| 21. |
- Salmon, Maelle, et al.
(författare)
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Monitoring Count Time Series in R : Aberration Detection in Public Health Surveillance
- 2016
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 70:10, s. 1-35
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Tidskriftsartikel (refereegranskat)abstract
- Public health surveillance aims at lessening disease burden by, e.g., timely recognizing emerging outbreaks in case of infectious diseases. Seen from a statistical perspective, this implies the use of appropriate methods for monitoring time series of aggregated case reports. This paper presents the tools for such automatic aberration detection offered by the R package surveillance. We introduce the functionalities for the visualization, modeling and monitoring of surveillance time series. With respect to modeling we focus on univariate time series modeling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. Applications of such modeling include illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g., by spline-modeling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdispersion using beta-binomial or Dirichlet-multinomial modeling. With respect to monitoring we consider detectors based on either a Shewhart-like single timepoint comparison between the observed count and the predictive distribution or by likelihood-ratio based cumulative sum methods. Finally, we illustrate how surveillance can support aberration detection in practice by integrating it into the monitoring workflow of a public health institution. Altogether, the present article shows how well surveillance can support automatic aberration detection in a public health surveillance context.
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| 22. |
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| 23. |
- Wittek, Peter, et al.
(författare)
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Somoclu : An Efficient Parallel Library for Self-Organizing Maps
- 2017
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 78:9
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Tidskriftsartikel (refereegranskat)abstract
- Somoclu is a massively parallel tool for training self-organizing maps on large data sets written in C++. It builds on OpenMP for multicore execution, and on MPI for distributing the workload across the nodes in a cluster. It is also able to boost training by using CUDA if graphics processing units are available. A sparse kernel is included, which is useful for high-dimensional but sparse data, such as the vector spaces common in text mining workflows. Python, R and MATLAB interfaces facilitate interactive use. Apart from fast execution, memory use is highly optimized, enabling training large emergent maps even on a single computer.
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