The R package hyreg2 introduces a frequentist framework for estimating latent class models for mixed outcome types using a joint likelihood approach. The method combines continuous and dichotomous data under the assumption that both outcome types arise from a common underlying data-generating process. In the implemented model, continuous responses are assumed to follow a normal distribution, while dichotomous responses are modeled using a binomial distribution. Such models are used in various scientific disciplines to estimate a common set of parameters across different types of data (e.g. clinical trials, econometrics and health economics). Latent class estimation is performed using the expectation-maximization algorithm as implemented in the widely used R package flexmix. The hyreg2 package offers a user-friendly implementation of this joint likelihood framework, allowing users to estimate models without explicitly programming the likelihood function. Heteroskedasticity as well as censored data can be taken into account. In addition to model estimation, the package provides dedicated summary and visualization functions to facilitate the interpretation of results. The article presents the methodological framework underlying the package and illustrates its functionality through an example based on the estimation of an EQ-5D-5L value set.

翻译：R包hyreg2引入了一个基于联合似然方法的频率学派框架，用于估计混合结局类型的潜类别模型。该方法在假设两种结局类型均源自同一潜在数据生成过程的前提下，结合连续变量和二分类数据。在实现的模型中，连续响应假设服从正态分布，而二分类响应则使用二项分布建模。此类模型在多个科学学科中用于估计不同数据类型（例如临床试验、计量经济学和健康经济学）的共同参数集。潜类别估计通过广泛使用的R包flexmix中实现的期望最大化算法进行。hyreg2包提供了该联合似然框架的用户友好实现，用户无需显式编写似然函数即可估计模型。模型可考虑异方差性和删失数据。除模型估计外，该包还提供了专用的汇总和可视化函数以促进结果解读。本文阐述了该包的方法学框架，并通过基于EQ-5D-5L效用值集估计的示例展示其功能。