  Regression models that incorporate smooth functions of predictor variables to
explain the relationships with a response variable have gained widespread usage
and proved successful in various applications. By incorporating smooth
functions of predictor variables, these models can capture complex
relationships between the response and predictors while still allowing for
interpretation of the results. In situations where the relationships between a
response variable and predictors are explored, it is not uncommon to assume
that these relationships adhere to certain shape constraints. Examples of such
constraints include monotonicity and convexity. The scam package for R has
become a popular package to carry out the full fitting of exponential family
generalized additive modelling with shape restrictions on smooths. The paper
aims to extend the existing framework of shape-constrained generalized additive
models (SCAM) to accommodate smooth interactions of covariates, linear
functionals of shape-constrained smooths and incorporation of residual
autocorrelation. The methods described in this paper are implemented in the
recent version of the package scam, available on the Comprehensive R Archive
Network (CRAN).
