Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.

Provides a 'Shiny' input widget, pasteBoxInput, 
    that allows users to paste images directly into a 'Shiny' 
    application. The pasted images are captured as Base64 
    encoded strings and can be used within the application 
    for various purposes, such as display or further processing. 
    This package is particularly useful for applications that 
    require easy and quick image uploads without the need for 
    traditional file selection dialog boxes.

Performant interactive scatterplot for ~ 1 million points. Zoom, pan,
    and pick points. Includes tooltips, labels, a grid overlay, legend, and
    coupled interactions across multiple plots.

Computation of predictive information criteria (PIC) from select model object classes 
    for model selection in predictive contexts. In contrast to the more widely used Akaike 
    Information Criterion (AIC), which are derived under the assumption that target(s) of prediction 
    (i.e. validation data) are independently and identically distributed to the fitting data, the PIC
    are derived under less restrictive assumptions and thus generalize AIC to the more practically 
    relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) <https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST> "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity".