The analysis of environmental data often requires
	     the detection of trends and change-points. 
	     This package includes tests for trend detection
	     (Cox-Stuart Trend Test, Mann-Kendall Trend Test, 
	     (correlated) Hirsch-Slack Test,
             partial Mann-Kendall Trend Test, multivariate (multisite)
	     Mann-Kendall Trend Test, (Seasonal) Sen's slope, 
	     partial Pearson and Spearman correlation trend test),
             change-point detection (Lanzante's test procedures, 
	     Pettitt's test, Buishand Range Test,
	     Buishand U Test, Standard Normal Homogeinity Test),
	     detection of non-randomness (Wallis-Moore Phase Frequency Test,
	     Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test)
	     and the two sample Robust Rank-Order Distributional Test.

Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series.
  Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>.
  Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. 
  Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.

Provides a coherent interface for evaluating models fit with the
  trending package.  This package is part of the RECON
  (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Provides a coherent interface to multiple modelling tools for
  fitting trends along with a standardised approach for generating confidence
  and prediction intervals.

Estimation of causal odds ratio and power calculation given trends in exposure prevalence    and outcome frequencies of stratified data.