The hier.part package for R is an implementation of the hierarchical partitioning algorithm published by Chevan and Sutherland (1991: The American Statistician 45: 90). The function hier.part() partitions the independent and joint contributions of each predictor in a multivariate data set to a linear model by hierarchical decomposition of goodness-of-fit measures of regressions. It uses goodness-of-fit measures for the full hierarchy of models based on N predictors (i.e., model (1), (2), …, (N), (1,2), …, (1,N), …, (1,2,3,…,N)). The function rand.hp() performs a randomization test that allows an Z-score assessment of the ‘importance’ of each predictor in explaining variation in the response variable.

The current version on CRAN is 1.0-5 (commit 8f8865da6a). A beta version of 1.0-6 (minor corrections) on github

Through CRAN or:

```
#logistic regression of an amphipod species occurrence in streams against
#four independent variables describing catchment characteristics
#(from Walsh et al (2004) Biodiversity and Conservation 13:781)
data(amphipod)
env <- amphipod[,2:5]
hier.part(amphipod$australis, env, fam = "binomial", gof = "logLik")
# shows that fconn (drainage connection) is the strongest independent
# predictor explaining amphipod occurrence (having elsewhere tested
# that the model predicts occurrence well).
rand.hp(amphipod$australis, env, fam = "binomial",
gof = "logLik", num.reps = 999)$Iprobs
#Z-scores suggest that fconn and fimp (imperviousness) are both
#important' independent predictors of amphipod abundance.
#(999 randomizations takes a few minutes).
```