statgenHTP 1.0.6

The package has been extended by functions for modelling evolution of the genetic signal. This extension consists of functions fitSplineHDM() for fitting a hierarchical data model, predict.psHDM() for making predictions based on this model, and plot.psHDM() for plotting the results. The methods used are described in a new vignette.
A small bug in detectSingleOutMaize() is fixed. Observations with a missing value for one of the involved traits are no longer tagged as outliers.

statgenHTP 1.0.5

The removeSerieOut() function now has an extra argument reason allowing for restricting removal of outliers to one or more reason the outliers where tagged.
The plot function for serieOut objects now has an extra argument reason allowing for restricting the plotting of outliers to one or more reason the outliers where tagged.
The detectSerieOut() function is now able to handle plotIds with irregular naming, i.e. plotIds starting with a number.
The results of estimateSplineParameters() can now be plotted in a box plot and histogram.
In the estimateSplineParameters() function multiple parameters can now be estimated at once.
A bug in detectSerieOut() that caused slope outliers to be never detected is fixed.
A bug causing predictions to be made in fitSpline() for missing values at the beginning or end of a time course is fixed.
The detectSerieOut() function now checks for number of plotIds per plant in the correct location leading to a more user friendly error message.

Plotting second derivatives of fitted splines, plotted first derivatives. This is fixed now.
The detectSerieOut() function now uses an extra criterion for checking if time courses our outlying. See the function documentation and vignettes for a full explanation of this new criterion.
The parameter trait in the removeSerieOut() function is renamed to traits and now accepts a vector of traits that for which outlier values can be replaced by NA.
Plotting the output of detectSerieOut() now has an extra parameter geno.decomp to restrict the output to selected levels of the geno.decomp variable in the data.
Computing the area under the curve in estimateSplineParameters() now allows the specification of the time unit used on the x-axis.
Fitting models using SpATS is now done using the option centered = TRUE by default.