Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>. For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.

Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency.

We provide an efficient way to design and conduct psycholinguistic experiments for testing the performance of large language models. It simplifies the process of setting up experiments, and data collection via large language models' API, streamlining workflow for researchers in the field of machine behavior. For methodology details, see Duan, X., Li, S., & Cai, Z. G. (2023) <doi:10.31234/osf.io/ywtfd>.

Performs causal mediation analysis under confounding or correlated errors. This package includes a single level mediation model, a two-level mediation model, and a three-level mediation model for data with hierarchical structures. Under the two/three-level mediation model, the correlation parameter is identifiable and is estimated based on a hierarchical-likelihood, a marginal-likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2014), Estimating Mediation Effects under Correlated Errors with an Application to fMRI, <arXiv:1410.7217> for details.