We introduce the R package npmv that performs nonparametric inference for the comparison of multivariate data samples and provides the results in easy-to-understand, but statistically correct, language. Unlike in classical multivariate analysis of variance, multivariate normality is not required for the data. In fact, the different response variables may even be measured on different scales (binary, ordinal, quantitative). p values are calculated for overall tests (permutation tests and F approximations), and, using multiple testing algorithms which control the familywise error rate, significant subsets of response variables and factor levels are identified. The package may be used for low-or highdimensional data with small or with large sample sizes and many or few factor levels.

a b s t r a c tWe develop parametric and nonparametric bootstrap methods for multi-factor multivariate data, without assuming normality, and allowing for covariance matrices that are heterogeneous between groups. The newly proposed, general procedure includes several situations as special cases, such as the multivariate Behrens-Fisher problem, the multivariate one-way layout, as well as crossed and hierarchically nested two-way layouts. We derive the asymptotic distribution of the bootstrap tests for general factorial designs and evaluate their performance in an extensive comparative simulation study. For moderate sample sizes, the bootstrap approach provides an improvement to existing methods in particular for situations with nonnormal data and heterogeneous covariance matrices in unbalanced designs. For balanced designs, less computationally intensive alternatives based on approximate sampling distributions of multivariate tests can be recommended.

In the snowmelt dominated hydrology of arid western US landscapes, late summer low streamflow is the most vulnerable period for aquatic ecosystem habitats and trout populations. This study analyzes mean August discharge at 153 streams throughout the Central Rocky Mountains of North America (CRMs) for changes in discharge from 1950-2008. The purpose of this study was to determine if: (1) Mean August stream discharge values have decreased over the last half-century; (2) Low discharge values are occurring more frequently;(3) Climatic variables are influencing August discharge trends. Here we use a strict selection process to characterize gauging stations based on amount of anthropogenic impact in order to identify heavily impacted rivers and understand the relationship between climatic variables and discharge trends. Using historic United States Geologic Survey discharge data, we analyzed data for trends of 40-59 years. Combining of these records along with aerial photos and water rights records we selected gauging stations based on the length and continuity of discharge records and categorized each based on the amount of diversion. Variables that could potentially influence discharge such as change in vegetation and Pacific Decadal Oscillation (PDO) were examined, but we found that that both did not significantly influence August discharge patterns. Our analyses indicate that non-regulated watersheds are experiencing substantial declines in stream discharge and we have found that 89% of all non-regulated stations exhibit a declining slope. Additionally our results here indicate a significant (α≤0.10) decline in discharge from 1951-2008 for the CRMs. Correlations results at our pristine sites show a negative relationship between air temperatures and discharge and these results coupled with increasing air temperature trends pose serious concern for aquatic ecosystems in CRMs.

Wood is commonly used for residential heating, but there are limited evidence-based interventions for reducing wood smoke exposures in the indoor environment. The Asthma Randomized Trial of Indoor Wood Smoke (ARTIS) study was designed to assess the efficacy of residential interventions to reduce indoor PM exposure from wood stoves. As part of a three-arm randomized placebo-controlled trial, two household-level interventions were evaluated: wood stove changeouts and air filtration units. Exposure outcomes included indoor measures such as continuous PM, particle counts, and carbon monoxide. Median indoor PM concentration was 17.5 μg/m in wood-burning homes prior to interventions. No significant reductions in PM concentrations were observed in the 40 homes receiving the placebo filter intervention. Sixteen homes received the wood stove changeout and showed no significant changes in PM or particle counts. PM concentrations were reduced by 68% in the filter intervention homes. Relative to placebo, air filtration unit homes had an overall PM reduction of 63% (95% CI: 47-75%). Relative to the wood stove changeout, the filtration unit intervention was more efficacious and less expensive, yet compliance issues indicated a need for the evaluation of additional strategies for improving indoor air quality in homes using wood stoves.

Enforcing an outdoor smoking ban using a multiple component package increased compliance with the nonsmoking policy on a college campus.