The increase in life expectancy followed by the burden of chronic diseases contributes to disability at older ages. The estimation of how much chronic conditions contribute to disability can be useful to develop public health strategies to reduce the burden. This paper introduces the R package addhaz, which is based on the attribution method (Nusselder and Looman, 2004) to partition disability into the additive contributions of diseases using cross-sectional data. The R package includes tools to fit the additive hazard model, the core of the attribution method, to binary and multinomial outcomes. The models are fitted by maximizing the binomial and multinomial log-likelihood functions using constrained optimization. Wald and bootstrap confidence intervals can be obtained for the parameter estimates. Also, the contribution of diseases to the disability prevalence and their bootstrap confidence intervals can be estimated. An additional feature is the possibility to use parallel computing to obtain the bootstrap confidence intervals. In this manuscript, we illustrate the use of addhaz with several examples for the binomial and multinomial models, using the data from the Brazilian National Health Survey, 2013