Performs comparative bioavailability calculations for Average
Bioequivalence with Expanding Limits (ABEL). Implemented are 'Method A' /
'Method B' and the detection of outliers. If the design allows, assessment
of the empiric Type I Error and iteratively adjusting alpha to control the
consumer risk. Average Bioequivalence - optionally with a tighter (narrow
therapeutic index drugs) or wider acceptance range (South Africa: Cmax) -
is implemented as well.

Two-Way ANOVA-Like Method to Analyze Replicated Point Patterns
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design.

Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.

A set of demonstrations of debunking of flawed analytical
set-ups
clim.pact, cyclones, waveslim, lmtest, deSolve, R (>= 2.10)
A set of R-functions providing some demonstration of bad
methods, and hence debunks some claims made in the climate
science literature. Most of the flaws exposed are breaches of
common practice within statistics, and these demonstrations
show why such breaches result in invalid results.

A set of R-functions providing some demonstration of
inappropriate methods, and hence shed light on why some claims
made in the climate science literature are unconvincing. Hence,
these demonstrations serve as a study in 'agnotology' (the
study of how and why we do not know things). Most of the flaws
exposed are breaches of common practice within statistics, and
these demonstrations show why such breaches result in invalid
results.

A common problem faced by journal reviewers and authors is the
question of whether the results of a replication study are consistent with
the original published study. One solution to this problem is to examine
the effect size from the published study and generate the range of effect
sizes that could reasonably be obtained (due to random sampling) in a
replication attempt (i.e., calculate a replication interval). If a
replication effect size falls outside the replication interval then that
value could not have occurred to due the effects of sampling error alone.
Alternatively, if a replication effect size falls within the replication
interval then the replication results could have reasonably occurred due to
the effects of sampling error alone. This package has functions that
calculate the replication interval for two different types of effect sizes
(i.e., correlation, Cohen's d).