Function Collection related to Plotting and Hydrology
miscellaneous functions to: draw horizontal histograms, color scatterpoints by 3rd dimension, enhance log-axis plots, zoom in X11 graphics, use the unit hydrograph in a linear storage cascade, convert lists to data.frames, fit multiple functions by regression (power, reciprocal, exponential, logarithmic, polynomial, rational). Development version: https://github.com/BerryBoessenkool

Draw horizontal histograms, color scatterpoints by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, use the unit hydrograph in a linear storage cascade, convert lists to data.frames, fit multiple functions by regression (power, reciprocal, exponential, logarithmic, polynomial, rational).

person("Ryan Andrew", "Peterson",
email = "ryan-peterson@uiowa.edu",
role = c("aut", "cre"))
Estimate a suite of normalizing transformations, including
a new technique based on ranks which can guarantee normally distributed
transformed data if there are no ties: Ordered Quantile Normalization.
The package is built to estimate the best normalizing transformation for
a vector consistently and accurately. It implements the Box-Cox
transformation, the Yeo-Johnson transformation, three types of Lambert
WxF transformations, and the Ordered Quantile normalization
transformation.

Estimate a suite of normalizing transformations, including
a new technique based on ranks which can guarantee normally distributed
transformed data if there are no ties: ordered quantile normalization.
The package is built to estimate the best normalizing transformation for
a vector consistently and accurately. It implements the Box-Cox
transformation, the Yeo-Johnson transformation, three types of Lambert
WxF transformations, and the ordered quantile normalization
transformation.

Estimate a suite of normalizing transformations, including
a new adaptation of a technique based on ranks which can guarantee
normally distributed transformed data if there are no ties: ordered
quantile normalization (ORQ). ORQ normalization combines a rank-mapping
approach with a shifted logit approximation that allows
the transformation to work on data outside the original domain. It is
also able to handle new data within the original domain via linear
interpolation. The package is built to estimate the best normalizing
transformation for a vector consistently and accurately. It implements
the Box-Cox transformation, the Yeo-Johnson transformation, three types
of Lambert WxF transformations, and the ordered quantile normalization
transformation.