  High-dimensional prediction considers data with more variables than samples.
Generic research goals are to find the best predictor or to select variables.
Results may be improved by exploiting prior information in the form of co-data,
providing complementary data not on the samples, but on the variables. We
consider adaptive ridge penalised generalised linear and Cox models, in which
the variable specific ridge penalties are adapted to the co-data to give a
priori more weight to more important variables. The R-package ecpc originally
accommodated various and possibly multiple co-data sources, including
categorical co-data, i.e. groups of variables, and continuous co-data.
Continuous co-data, however, was handled by adaptive discretisation,
potentially inefficiently modelling and losing information. Here, we present an
extension to the method and software for generic co-data models, particularly
for continuous co-data. At the basis lies a classical linear regression model,
regressing prior variance weights on the co-data. Co-data variables are then
estimated with empirical Bayes moment estimation. After placing the estimation
procedure in the classical regression framework, extension to generalised
additive and shape constrained co-data models is straightforward. Besides, we
show how ridge penalties may be transformed to elastic net penalties with the
R-package squeezy. In simulation studies we first compare various co-data
models for continuous co-data from the extension to the original method.
Secondly, we compare variable selection performance to other variable selection
methods. Moreover, we demonstrate use of the package in several examples
throughout the paper.
