Title: Automated Covariate Selection Using HDPS Algorithm
Description: Contains functions to implement automated covariate selection using methods described in the
high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for
for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score
matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for
confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates
in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in.
The functions in this package help to implement the three major steps of automated covariate selection as described by
Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate
selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates().
In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is
also available for running examples and also for further exploration. The original article where the algorithm is described

Title: Automatic Construction of Forced-Choice Tests
person("Mengtong", "Li", email = "ml70@illinois.edu", role = c("cre", "aut"), comment = c(ORCID = "0000-0002-1766-4976")),
person("Tianjun", "Sun", email = "tsun5@illinois.edu", role = "aut", comment = c(ORCID = "0000-0002-3655-0042")),
person("Bo", "Zhang", email = "bozhang3065@gmail.com", role = "aut"))
Author: Mengtong Li [cre, aut] (<https://orcid.org/0000-0002-1766-4976>),
Description: Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 <doi:10.1177/1094428119836486>). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 <doi:10.31234/osf.io/hmnrc>). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) or minimize item location differences (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests, essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC tests can be automatically constructed based on user-defined pairing criteria and weights as well as customized optimization behavior. Users can also construct parallel forms of the same test following the same pairing rules.