Self-reported health, happiness, attitudes, and other statuses or 
 perceptions are often the subject of biases that may come from different 
 sources. For example, the evaluation of own health may depend on previous 
 medical diagnoses, functional status, and symptoms and signs of illness, as 
 well as life-style behaviors including contextual social, gender, age-specific,
 linguistic and other cultural factors (Jylha 2009 
 <doi:10.1016/j.socscimed.2009.05.013>; Oksuzyan et al. 2019 
 <doi:10.1016/j.socscimed.2019.03.002>). This package offers versatile functions
 for analyzing different self-reported ordinal variables and helping to estimate
 their biases.
 Specifically, the package provides the function to fit a generalized ordered 
 probit model that regresses original self-reported status measures on two sets 
 of independent variables (King et al. 2004 <doi:10.1017/S0003055403000881>; 
 Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan et al. 2019 
 <doi:10.1016/j.socscimed.2019.03.002>). In contrast to standard ordered probit 
 models, generalized ordered probit models relax the assumption that individuals
 use a common scale when rating their own statuses, and thus allow for 
 distinguishing between the status (e.g., health) and reporting differences 
 based on other individual characteristics. In other words, the model accounts 
 for heterogeneity in reporting behaviors. The first set of variables 
 (e.g., health variables) included in the regression are individual statuses and
 characteristics that are directly related to the self-reported variable. 
 In case of self-reported health, these could be chronic conditions, mobility 
 level, difficulties with daily activities, performance on grip strength tests, 
 anthropometric measures, and lifestyle behaviors. The second set of independent
 variables (threshold variables) is used to model cut-points between adjacent 
 self-reported response categories as functions of individual characteristics, 
 such as gender, age group, education, and country 
 (Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). 
 The model helps adjust for these specific socio-demographic and cultural 
 differences in how the continuous latent health is projected onto the ordinal 
 self-rated measure.
 The fitted model can be used to calculate an individual latent status variable 
 that serves as a proxy of the true status. In case of self-reported health, the
 predicted latent health variable can be standardized to a health index, which 
 varies from 0 representing the (model-based) worst health state to 1 
 representing the (model-based) best health in the sample. 
 The standardized latent coefficients (disability weights for the case of 
 self-rated health) provide information about the individual impact of the 
 specific latent (e.g., health) variables on the latent (e.g., health) 
 construct. For example, they indicate the extent to which the latent health 
 index is reduced by the presence of Parkinson’s disease, poor mobility, and 
 other specific health measures (Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan 
 et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>).
 The latent index can in turn be used to reclassify the categorical status 
 measure that has been adjusted for inter-individual differences in reporting 
 behavior. Two methods for doing so are available, one which uses model 
 estimated cut-points, and a second which reclassifies responses according to 
 the percentiles of the original categorical response distribution 
 (Jurges 2007 <doi:10.1002/hec.1134>; 
 Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>).