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M-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find roots and compute the empirical sandwich variance estimator for any set…

Applications · Statistics 2019-01-08 Bradley C. Saul , Michael G. Hudgens

The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted…

Machine Learning · Statistics 2018-01-29 Marko Nagode

A new formula for the probability that a standard Brownian motion stays between two linear boundaries is proved. A simple algorithm is deduced. Uniform precision estimates are computed. Different implementations have been made available…

Probability · Mathematics 2016-12-20 Bernard Ycart , Rémy Drouilhet

The R package BNSP provides a unified framework for semiparametric location-scale regression and stochastic search variable selection. The statistical methodology that the package is built upon utilizes basis function expansions to…

Other Statistics · Statistics 2018-10-09 Georgios Papageorgiou

robustloggamma is an R package for robust estimation and inference in the generalized loggamma model. We briefly introduce the model, the estimation procedures and the computational algorithms. Then, we illustrate the use of the package…

Computation · Statistics 2015-12-08 Claudio Agostinelli , Alfio Marazzi , Victor J. Yohai , Alex Randriamiharisoa

We introduce an R package, PCMBase, to rapidly calculate the likelihood for multivariate phylogenetic comparative methods. The package is not specific to particular models but offers the user the functionality to very easily implement a…

Populations and Evolution · Quantitative Biology 2019-12-13 Venelin Mitov , Krzysztof Bartoszek , Georgios Asimomitis , Tanja Stadler

We introduce a class of regularized M-estimators of multivariate scatter and show, analogous to the popular spatial sign covariance matrix (SSCM), that they possess high breakdown points. We also show that the SSCM can be viewed as an…

Methodology · Statistics 2023-08-01 David E. Tyler , Mengxi Yi , Klaus Nordhausen

The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the oversampling methodology implemented by the package. A tutorial of the…

Computation · Statistics 2017-11-28 Matthew Dixon , Diego Klabjan , Lan Wei

In the context of large samples, a small number of individuals might spoil basic statistical indicators like the mean. It is difficult to detect automatically these atypical individuals, and an alternative strategy is using robust…

Machine Learning · Statistics 2023-04-04 Antoine Godichon-Baggioni , Wei Lu

We generalize univariate multipoint evaluation of polynomials of degree n at sublinear amortized cost per point. More precisely, it is shown how to evaluate a bivariate polynomial p of maximum degree less than n, specified by its n^2…

Data Structures and Algorithms · Computer Science 2007-05-23 Michael Nüsken , Martin Ziegler

In \textit{computer-based testing} it has become standard to collect response accuracy (RA) and response times (RTs) for each test item. IRT models are used to measure a latent variable (e.g., ability, intelligence) using the RA…

Methodology · Statistics 2021-06-21 Jean-Paul Fox , Konrad Klotzke , Ahmet Salih Simsek

Multiple orthogonal polynomials satisfy a number of recurrence relations, in particular there is a $(r+2)$-term recurrence relation connecting the type II multiple orthogonal polynomials near the diagonal (the so-called step-line recurrence…

Classical Analysis and ODEs · Mathematics 2015-10-30 Galina Filipuk , Maciej Haneczok , Walter Van Assche

This paper describes and illustrates the functionality of the baker R package. The package estimates a suite of nested partially-latent class models (NPLCM) for multivariate binary responses that are observed under a case-control design.…

Methodology · Statistics 2022-02-25 Irena B Chen , Qiyuan Shi , Scott L Zeger , Zhenke Wu

Bayesian statistics is an integral part of contemporary applied science. bayesics provides a single framework, unified in syntax and output, for performing the most commonly used statistical procedures, ranging from one- and two-sample…

Methodology · Statistics 2026-02-18 Daniel K. Sewell , Alan T. Arakkal

Detecting changepoints in a time series of length $N$ entails evaluating up to $2^{N-1}$ possible changepoint models, making exhaustive enumeration computationally infeasible. Genetic algorithms (GAs) provide a stochastic way to identify…

Computation · Statistics 2025-09-30 Mo Li , QiQi Lu

Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian…

Machine Learning · Statistics 2019-05-14 Reza Mohammadi , Ernst C. Wit

It is shown how to set up, conduct, and analyze large simulation studies with the new R package simsalapar = simulations simplified and launched parallel. A simulation study typically starts with determining a collection of input variables…

Computation · Statistics 2013-09-18 Marius Hofert , Martin Mächler

This work presents a guide for the use of some of the functions of the multiColl package in R for the detection of near-multicollinearity. The main contribution, in comparison to other existing packages in R or other econometric software,…

Computation · Statistics 2021-07-08 R. Salmerón , C. B. García , J. García

Ordinal scores occur commonly in medical imaging studies and in black-box forensic studies \citep{Phillips:2018}. To assess the accuracy of raters in the studies, one needs to estimate the receiver operating characteristic (ROC) curve while…

Applications · Statistics 2023-07-19 Ngoc-Ty Nguyen , P. Jonathon Phillips , Larry Tang

In this paper, we introduce a new variant of the $p$-median facility location problem in which it is assumed that the exact location of the potential facilities is unknown. Instead, each of the facilities must be located in a region around…

Optimization and Control · Mathematics 2017-10-24 Víctor Blanco