  The matched case-control design, up until recently mostly pertinent to
epidemiological studies, is becoming customary in biomedical applications as
well. For instance, in omics studies, it is quite common to compare cancer and
healthy tissue from the same patient. Furthermore, researchers today routinely
collect data from various and variable sources that they wish to relate to the
case-control status. This highlights the need to develop and implement
statistical methods that can take these tendencies into account. We present an
R package penalizedclr, that provides an implementation of the penalized
conditional logistic regression model for analyzing matched case-control
studies. It allows for different penalties for different blocks of covariates,
and it is therefore particularly useful in the presence of multi-source omics
data. Both L1 and L2 penalties are implemented. Additionally, the package
implements stability selection for variable selection in the considered
regression model. The proposed method fills a gap in the available software for
fitting high-dimensional conditional logistic regression model accounting for
the matched design and block structure of predictors/features. The output
consists of a set of selected variables that are significantly associated with
case-control status. These features can then be investigated in terms of
functional interpretation or validation in further, more targeted studies.
