T he empirical literature that examines cross-national patterns of state repression seeks to discover a set of political, economic, and social conditions that are consistently associated with government violations of human rights. Null hypothesis significance testing is the most common way of examining the relationship between repression and concepts of interest, but we argue that it is inadequate for this goal, and has produced potentially misleading results. To remedy this deficiency in the literature we use cross-validation and random forests to determine the predictive power of measures of concepts the literature identifies as important causes of repression. We find that few of these measures are able to substantially improve the predictive power of statistical models of repression. Further, the most studied concept in the literature, democratic political institutions, predicts certain kinds of repression much more accurately than others. We argue that this is due to conceptual and operational overlap between democracy and certain kinds of state repression. Finally, we argue that the impressive performance of certain features of domestic legal systems, as well as some economic and demographic factors, justifies a stronger focus on these concepts in future studies of repression.

SummaryThis package contains functions useful for exploratory data analysis using random forests, which can be fit using the randomForest, randomForestSRC, or party packages (Liaw and Wiener 2002;Ishwaran and Kogalur 2013;Hothorn, Hornik, and Zeileis 2006). These functions can compute the partial dependence of covariates (individually or in combination) on the fitted forests' predictions, the permutation importance of covariates, as well as the distance between data points according to the fitted model.Random forests are an attractive method for social scientists. Random forests have only a few important tuning parameters and can be adapted to do classification, regression, and clustering. Many research tasks require interpretable models, and, although methods for interpreting random forests exist, in the most popular packages for fitting random forests these methods are available inconsistently. For example although there are methods for computing permutation importance in the randomForest, randomForestSRC and party packages, only randomForest can compute local importance. None of the packages can compute permutation importance for groups of covariates. Similarly partial dependence is only implemented in randomForest, and it has limited functionality compared to the functions provided herein. This software has been used in Jones and Linder (2015); Jones and Lupu (2016). Partial dependence, as described by Friedman (2001), estimates the marginal relationship between a subset of the covariates and the model's predictions by averaging over the marginal distribution of the compliment of this subset of the covariates. This approximation allows the display of the relationship between this subset of the covariates and the model's predictions even when there are many covariates which may interact. This functionality works with models fit by any of the aforementioned packages to any of the supported types of outcome variables. partial_dependence can be parallelized and also contains a number of additional parameters which allow the user to control this approximation. There is an associated plot function plot_pd which constructs plots for a wide variety of possible outputs from partial_dependence (e.g., when pairs of covariates are considered jointly, when each covariate is considered separately, when the outcome variable is categorical, etc).Permutation importance estimates the importance of a covariate by randomly shuffling its values, breaking any dependence between said covariate and the outcome, and then computing the difference between the predictions made by the model with that covariate shuffled and the predictions made when the covariate was not shuffled. If the covariate was useful in generating predictions then the prediction errors will increase in expectation when the covariate is shuffled, whereas no such increase can be expected when the covariate has no influence. Although all three of the random forest packages provide at least one method of assessing variable importance, variable_importance provides a c...

SUMMARYNatural branches vary conspicuously in their diameter, density and orientation, but how these latter two factors affect animal locomotion is poorly understood. Thus, for three species of arboreal anole lizards found on different size branches and with different limb lengths, we tested sprinting performance on cylinders with five diameters (5-100mm) and five patterns of pegs, which simulated different branch orientations and spacing. We also tested whether the lizards preferred surfaces that enhanced their performance. The overall responses to different surfaces were similar among the three species, although the magnitude of the effects differed. All species were faster on cylinders with larger diameter and no pegs along the top. The short-limbed species was the slowest on all surfaces. Much of the variation in performance resulted from variable amounts of pausing among different surfaces and species. Lizards preferred to run along the top of cylinders, but pegs along the top of the narrow cylinders interfered with this. Pegs on top of the 100-mm diameter cylinder, however, had little effect on speed as the lizards ran quite a straight path alongside pegs without bumping into them. All three species usually chose surfaces with greater diameters and fewer pegs, but very large diameters with pegs were preferred to much smaller diameter cylinders without pegs. Our results suggest that preferring larger diameters in natural vegetation has a direct benefit for speed and an added benefit of allowing detouring around branches with little adverse effect on speed. Supplementary material available online at

Is there more violence in the middle? Over 100 studies have analyzed whether violent outcomes such as civil war, terrorism, and repression are more common in regimes that are neither full autocracies nor full democracies, yet findings are inconclusive. While this hypothesis is ultimately about functional form, existing work uses models in which a particular functional form is assumed. Existing work also uses arbitrary operationalizations of “the middle.” This article aims to resolve the empirical uncertainty about this relationship by using a research design that overcomes the limitations of existing work. We use a random forest‐like ensemble of multivariate regression and classification trees to predict multiple forms of conflict. Our results indicate the specific conditions under which there is or is not more violence in the middle. We find the most consistent support for the hypothesis with respect to minor civil conflict and no support with respect to repression.

This paper presents the first use of bone collagen stable isotope analyses for the purpose of reconstructing historical animal husbandry and trade practices in Australia. Stable carbon and nitrogen isotope analyses of 51 domesticate and commensal specimens demonstrate that meats consumed at the mid to late nineteenth-century Commonwealth Block site in Melbourne derived from animals with a diverse range of isotopic signatures. Potential factors contributing to this diversity including animal trade and variability in local animal husbandry practices are discussed. From these results we suggest that stable isotope-based paleodietary reconstructions have significant potential to illuminate a variety of human-animal relations in Australia's historical period as well as other New World contexts.