PI-Change is a prior-informed multiple change point detection
    method that incorporates prespecified plausible change point locations
    through a time-varying penalized likelihood. The method extends multiple
    change point detection beyond purely data-driven segmentation by allowing
    external knowledge about plausible change point locations to enter the
    objective function; see Jacobs and Chen (2026)
    <doi:10.48550/arXiv.2605.01003>.

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.

Deduplicates datasets by retaining the most complete and informative records. Identifies duplicated entries based on a specified key column, calculates completeness scores for each row, and compares values within groups. When differences between duplicates exceed a user-defined threshold, records are split into unique IDs; otherwise, they are coalesced into a single, most complete entry. Returns a list containing the original duplicates, the split entries, and the final coalesced dataset. Useful for cleaning survey or administrative data where duplicated IDs may reflect minor data entry inconsistencies.

High Dynamic Range (HDR) images support a large range in luminosity
    between the lightest and darkest regions of an image.  To capture this range,
    data in HDR images is often stored as floating point numbers and in formats
    that capture more data and channels than standard image types.  This package supports
    reading and writing two types of HDR images; PFM (Portable Float Map) 
    and OpenEXR images. HDR images can be converted to lower
    dynamic ranges (for viewing) using tone-mapping.  A number of
    tone-mapping algorithms are included which are based on 
    Reinhard (2002) "Photographic tone reproduction for digital images" 
    <doi:10.1145/566654.566575>.

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".