Martin Mächler

Bio: Martin Mächler is an academic researcher from ETH Zurich. The author has contributed to research in topics: Generalized linear mixed model & Copula (probability theory). The author has an hindex of 15, co-authored 27 publications receiving 42993 citations. Previous affiliations of Martin Mächler include École Polytechnique Fédérale de Lausanne.

Variable length Markov chains: methodology, computing and software

Abstract: This article presents a tutorial and new, publicly available computational tools for variable length Markov chains (VLMC). VLMCs are Markov chains with the additional attractive structure that their memories depend on a variable number of lagged values, depending on what the actual past (the lagged values) looks like. They build a very flexible class of tree-structured models for categorical time series. Fitting VLMCs from data is a nontrivial computational task. We provide an efficient implementation of the so-called context algorithm which requires only O(n log(n)) operations. The implementation, which is publicly available, includes additional important new features and options: diagnostics, goodness of fit, simulation and bootstrap, residuals, and tuning the context algorithm. Our tutorial is presented with a version in R which is available from the Comprehensive R Archive Network (CRAN). The exposition is self-contained, gives rigorous and partly new mathematical descriptions, and is illustrated by a...

Rmetrics - timeDate Package

Abstract: The management of time and holidays can prove crucial in applications that rely on his- torical data. A typical example is the aggregation of a data set recorded in different time zones and under different daylight saving time rules. Be- sides the time zone conversion function, which is well supported by default classes in R, one might need functions to handle special days or holi- days. In this respect, the package timeDate en- hances default date-time classes in R and brings new functionalities to time zone management and the creation of holiday calendars.

Ties, Time Series, and Regression

Abstract: This chapter is concerned with more advanced topics in copula modeling such as the handling of ties, time series, and covariates (in a regression-like setting).

Statistics: An Introduction using R

Abstract: you cannot undo your mistakes, except to start afresh. More formally, there is no representation of the graphics independent of their presence on the plot so you can only add, not edit or delete, existing output. This makes base graphics simple and easy to understand but fundamentally limited. This limitation is best seen when trying to customize graphics, where you either need to start from scratch or grapple with many arcane settings. R Graphics provides an excellent summary of these details. In terms of functionality, but not yet popularity, base graphics has been superseded by the grid graphics system. The explanation of grid is the strength of this book. The section begins with a description of lattice graphics, a system that illustrates the power of grid. Lattice is an implementation of trellis graphics (Becker, Cleveland, and Shyu 1996), which provides an easy way to produce multiple plots based on different subsets of a dataset. The plots typically share the same scales and allow one to investigate relationships between two variables conditional on one (or more) other variables. Lattice graphics present a higher level of abstraction than base graphics, but configuring lattice can be difficult due to the multitudinous (378 at last count) and repetitive options. This book provides a handy reference to some of these options as well as a brief discussion of annotating lattice plots. The description of creating new plot types is briefer still, largely a reflection of the limitations of lattice. The framework provided by grid graphics has a number of advantages over the ink on paper design of base graphics:

Scatterplot3d - an R package for visualizing multivariate data

Abstract: Scatterplot3d is an R package for the visualization of multivariate data in a three dimensional space. R is a “language for data analysis and graphics”. In this paper we discuss the features of the package. It is designed by exclusively making use of already existing functions of R and its graphics system and thus shows the extensibility of the R graphics system. Additionally some examples on generated and real world data are provided, as well as the source code and the help page of scatterplot3d.

lmerTest Package: Tests in Linear Mixed Effects Models

Abstract: One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects - both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.

glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling

Abstract: Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions We present a new package, glmmTMB, and compare it to other R packages that fit zero-inflated mixed models The glmmTMB package fits many types of GLMMs and extensions, including models with continuously distributed responses, but here we focus on count responses glmmTMB is faster than glmmADMB, MCMCglmm, and brms, and more flexible than INLA and mgcv for zero-inflated modeling One unique feature of glmmTMB (among packages that fit zero-inflated mixed models) is its ability to estimate the Conway-Maxwell-Poisson distribution parameterized by the mean Overall, its most appealing features for new users may be the combination of speed, flexibility, and its interface’s similarity to lme4

brms: An R Package for Bayesian Multilevel Models Using Stan

Abstract: The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit - among others - linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include autocorrelation of the response variable, user defined covariance structures, censored data, as well as meta-analytic standard errors. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with the Watanabe-Akaike information criterion and leave-one-out cross-validation.

The effect of human mobility and control measures on the COVID-19 epidemic in China.

Abstract: The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.

Qgraph: Network visualizations of relationships in psychometric data

Abstract: We present the qgraph package for R, which provides an interface to visualize data through network modeling techniques. For instance, a correlation matrix can be represented as a network in which each variable is a node and each correlation an edge; by varying the width of the edges according to the magnitude of the correlation, the structure of the correlation matrix can be visualized. A wide variety of matrices that are used in statistics can be represented in this fashion, for example matrices that contain (implied) covariances, factor loadings, regression parameters and p values. qgraph can also be used as a psychometric tool, as it performs exploratory and confirmatory factor analysis, using sem and lavaan; the output of these packages is automatically visualized in qgraph ,w hich may aid the interpretation of results. In this article, we introduce qgraph by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.