Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).

This paper introduces JASP, a free graphical software package for basic statistical procedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. JASP is open-source and differentiates itself from existing open-source solutions in two ways. First, JASP provides several innovations in user interface design; specifically, results are provided immediately as the user makes changes to options, output is attractive, minimalist, and designed around the principle of progressive disclosure, and analyses can be peer reviewed without requiring a "syntax". Second, JASP provides some of the recent developments in Bayesian hypothesis testing and Bayesian parameter estimation. The ease with which these relatively complex Bayesian techniques are available in JASP encourages their broader adoption and furthers a more inclusive statistical reporting practice. The JASP analyses are implemented in R and a series of R packages.

Question: Quantification of the effect of species traits on the assembly of communities is challenging from a statistical point of view. A key question is how species occurrence and abundance can be explained by the traits values of the species and the environmental values at the sites. Methods:Using a sites x species abundance table, a site x environment data table and a species x trait data table, we address this question by a novel Generalized linear mixed model (GLMM) approach. The GLMM overcomes the problem of pseudoreplication and heteroscedastic variance by including sites and species as random factors. The method is equally well applicable to presence-absence data as to count and multinomial data. We present a tiered forward selection approach for obtaining a parsimonious model and compare the results with the fourth corner method and RLQ ordination. Results:We illustrate the approach on a presence-absence version on two well-known data sets. In the Dune Meadow data species presence is parsimoniously explained by moisture and manure of the meadows in combination with seed mass and specific leaf area, respectively. In the Grazed Grassland data species presence is parsimoniously explained by the grazing intensity and soil phosphorous in combination with the C:N ratio and flowering mode, respectively. Conclusions:Our GLMM approach can be used to identify which species traits and environmental variables best explain the species distribution, and which traits are significantly correlated with environmental variables. The method is better suited for providing an interpretable and predictive model than the fourth corner method and RLQ.