| 1. |
- Andersson, Björn, 1984-, et al.
(author)
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Performing the Kernel Method of Test Equating with the Package kequate
- 2013
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In: Journal of Statistical Software. - : The American Statistical Association. - 1548-7660. ; 55:6, s. 1-25
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Journal article (peer-reviewed)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.
(author)
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Calculating Probabilistic Excursion Sets and Related Quantities Using excursions
- 2018
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In: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 86:5, s. 1-20
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Journal article (peer-reviewed)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.
(author)
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Developer-Friendly and Computationally Efficient Predictive Modeling without Information Leakage : The emil Package for R
- 2018
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In: Journal of Statistical Software. - : JOURNAL STATISTICAL SOFTWARE. - 1548-7660. ; 85:13, s. 1-30
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Journal article (peer-reviewed)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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| 5. |
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| 6. |
- Dahlin, Johan, 1986-, et al.
(author)
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Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models
- 2019
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In: Journal of Statistical Software. - Alexandria, VA, United States : American Statistical Association. - 1548-7660. ; 88:CN2, s. 1-41
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Journal article (peer-reviewed)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
(author)
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CMregr - A Matlab software package for finding CM-Estimates for Regression
- 2004
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In: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 10:3, s. 1-11
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Journal article (peer-reviewed)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
(author)
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KFAS : Exponential Family State Space Models in R
- 2017
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In: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 78:10
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Journal article (peer-reviewed)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.
(author)
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Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
- 2019
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In: Journal of Statistical Software. - Alexandria, VA, United States : American Statistical Association. - 1548-7660. ; 88:3, s. 1-32
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Journal article (peer-reviewed)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.
(author)
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CovSel : An R Package for Covariate Selection When Estimating Average Causal Effects
- 2015
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In: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 68:1, s. 1-20
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Journal article (peer-reviewed)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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