In this paper, an extension to allow the presence of non-informative left- or
right-censored observations in log-symmetric regression models is addressed. Under
such models, the log-lifetime distribution belongs to the symmetric class and its loca-
tion and scale parameters are described by semi-parametric functions of explanatory
variables, whose nonparametric components are approximated using natural cubic
splines or P-splines. An iterative process of parameter estimation by the maximum
penalized likelihood method is presented. The large sample properties of the maximum
penalized likelihood estimators are studied analytically and by simulation experiments.
Diagnostic methods such as deviance-type residuals and local influence measures are
derived. The package ssym, which includes an implementation in the computational
environment R of the methodology addressed in this paper, is also discussed. The
proposed methodology is illustrated by the analysis of a real data set.

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• Log-symmetric regression models under the presence of non-informative left- or right-censored observations