Journal of Statistical Software
Not a member yet
1629 research outputs found
Sort by
cpop: Detecting Changes in Piecewise-Linear Signals
Get PDFChangepoint detection is an important problem with a wide range of applications. There are many different types of changes that one may wish to detect, and a widerange of algorithms and software for detecting them. However there are relatively few approaches for detecting changes-in-slope in the mean of a signal plus noise model. We describe the R package cpop, available on the Comprehensive R Archive Network (CRAN). This package implements CPOP, a dynamic programming algorithm, to find the optimal set of changes that minimizes an L0 penalized cost, with the cost being a weighted residual sum of squares. The package has extended the CPOP algorithm so it can analyse data that is unevenly spaced, allow for heterogeneous noise variance, and allows for a grid of potential change locations to be different from the locations of the data points. There is also an implementation that uses the CROPS algorithm to detect all segmentations that are optimal as you vary the L0 penalty for adding a change across a continuous range of values
mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R
Get PDFSpatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. This contribution provides an overview of the state-of-the-art in spatial and spatiotemporal cross-validation techniques and their implementations in R while introducing the R package mlr3spatiotempcv as an extension package of the machine-learning framework mlr3. Currently various R packages implementing different spatiotemporal partitioning strategies exist: blockCV, CAST, skmeans and sperrorest. The goal of mlr3spatiotempcv is to gather the available spatiotemporal resampling methods in R and make them available to users through a simple and common interface. This is made possible by integrating the package directly into the mlr3 machine-learning framework, which already has support for generic non-spatiotemporal resampling methods such as random partitioning. One advantage is the use of a consistent nomenclature in an overarching machine-learning toolkit instead of a varying package-specific syntax, making it easier for users to choose from a variety of spatiotemporal resampling methods. This package avoids giving recommendations which method to use in practice as this decision depends on the predictive task at hand, the autocorrelation within the data, and the spatial structure of the sampling design or geographic objects being studied
spsurvey: Spatial Sampling Design and Analysis in R
Get PDFspsurvey is an R package for design-based statistical inference, with a focus on spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) algorithm to select spatially balanced samples via the grts() function. The grts() function flexibly accommodates several sampling design features, including stratification, varying inclusion probabilities, legacy (or historical) sites, minimum distances between sites, and two options for replacement sites. spsurvey also provides a suite of data analysis options, including categorical variable analysis (cat_analysis()), continuous variable analysis (cont_analysis()), relative risk analysis (relrisk_analysis()), attributable risk analysis (attrisk_analysis()), difference in risk analysis (diffrisk_analysis()), change analysis (change_analysis()), and trend analysis (trend_analysis()). In this manuscript, we first provide background for the GRTS algorithm and the analysis approaches and then show how to implement them in spsurvey. We find that the spatially balanced GRTS algorithm yields more precise parameter estimates than simple random sampling, which ignores spatial information
logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations
Get PDFThis paper introduces the logitr R package for fast maximum likelihood estimation of multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals according to a chosen distribution. The package is faster than other similar packages such as mlogit, gmnl, mixl, and apollo, and it supports utility models specified with “preference space” or “willingness-to-pay (WTP) space” parameterizations, allowing for the direct estimation of marginal WTP. The typical procedure of computing WTP post-estimation using a preference space model can lead to unreasonable distributions of WTP across the population in mixed logit models. The paper provides a discussion of some of the implications of each utility parameterization for WTP estimates. It also highlights some of the design features that enable logitr's performant estimation speed and includes a benchmarking exercise with similar packages. Finally, the paper highlights additional features that are designed specifically for WTP space models, including a consistent user interface for specifying models in either space and a parallelized multi-start optimization loop, which is particularly useful for searching the solution space for different local minima when estimating models with non-convex log-likelihood functions
varTestnlme: An R Package for Variance Components Testing in Linear and Nonlinear Mixed-Effects Models
Get PDFThe issue of variance components testing arises naturally when building mixed-effects models, to decide which effects should be modeled as fixed or random or to build parsimonious models. While tests for fixed effects are available in R for models fitted with lme4, tools are missing when it comes to random effects. The varTestnlme package for R aims at filling this gap. It allows to test whether a subset of the variances and covariances corresponding to a subset of the random effects, are equal to zero using asymptotic property of the likelihood ratio test statistic. It also offers the possibility to test simultaneously for fixed effects and variance components. It can be used for linear, generalized linear or nonlinear mixed-effects models fitted via lme4, nlme or saemix. Numerical methods used to implement the test procedure are detailed and examples based on different real datasets using different mixed models are provided. Theoretical properties of the used likelihood ratio test are recalled
Panel Data Visualization in R (panelView) and Stata (panelview)
Get PDFWe develop an R package panelView and a Stata package panelview for panel data visualization. They are designed to assist causal analysis with panel data and have three main functionalities: (1) They plot the treatment status and missing values in a panel dataset; (2) they visualize the temporal dynamics of the main variables of interest; and (3) they depict the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. These tools can help researchers better understand their panel datasets before conducting statistical analysis
ARCHModels.jl: Estimating ARCH Models in Julia
Get PDFThis paper introduces ARCHModels.jl, a package for the Julia programming language that implements a number of univariate and multivariate autoregressive conditional heteroskedasticity models. This model class is the workhorse tool for modeling the conditional volatility of financial assets. The distinguishing feature of these models is that they model the latent volatility as a (deterministic) function of past returns and volatilities. This recursive structure results in loop-heavy code which, due to its just-in-time compiler, Julia is well-equipped to handle. As such, the entire package is written in Julia, without any binary dependencies. We benchmark the performance of ARCHModels.jl against popular implementations in MATLAB, R, and Python, and illustrate its use in a detailed case study
hdpGLM: An R Package to Estimate Heterogeneous Effects in Generalized Linear Models Using Hierarchical Dirichlet Process
Get PDFThe existence of latent clusters with different responses to a treatment is a major concern in scientific research, as latent effect heterogeneity often emerges due to latent or unobserved features - e.g., genetic characteristics, personality traits, or hidden motivations - of the subjects. Conventional random- and fixed-effects methods cannot be applied to that heterogeneity if the group markers associated with that heterogeneity are latent or unobserved. Alternative methods that combine regression models and clustering procedures using Dirichlet process are available, but these methods are complex to implement, especially for non-linear regression models with discrete or binary outcomes. This article discusses the R package hdpGLM as a means of implementing a novel hierarchical Dirichlet process approach to estimate mixtures of generalized linear models outlined in Ferrari (2020). The methods implemented make it easy for researchers to investigate heterogeneity in the effect of treatment or background variables and identify clusters of subjects with differential effects. This package provides several features for out-of-the-box estimation and to generate numerical summaries and visualizations of the results. A comparison with other similar R packages is provided
Regression Modeling for Recurrent Events Possibly with an Informative Terminal Event Using R Package reReg
Get PDFRecurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package reReg offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, possibly with the presence of an informative terminal event. The regression framework is a general scalechange model which encompasses the popular Cox-type model, the accelerated rate model, and the accelerated mean model as special cases. Informative censoring is accommodated through a subject-specific frailty without any need for parametric specification. Different regression models are allowed for the recurrent event process and the terminal event. Also included are visualization and simulation tools
Broken Stick Model for Irregular Longitudinal Data
Get PDFMany longitudinal studies collect data that have irregular observation times, often requiring the application of linear mixed models with time-varying outcomes. This paper presents an alternative that splits the quantitative analysis into two steps. The first step converts irregularly observed data into a set of repeated measures through the broken stick model. The second step estimates the parameters of scientific interest from the repeated measurements at the subject level. The broken stick model approximates each subject's trajectory by a series of connected straight lines. The breakpoints, specified by the user, divide the time axis into consecutive intervals common to all subjects. Specification of the model requires just three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: Subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The R package brokenstick v2.5.0 offers tools to calculate, predict, impute and visualize broken stick estimates. The package supports two optimization methods, including options to constrain the variance-covariance matrix of the random effects. We demonstrate six applications of the model: Detection of critical periods, estimation of the time-to-time correlations, profile analysis, curve interpolation, multiple imputation and personalized prediction of future outcomes by curve matching