A pipe-friendly R client for PX-Web statistical APIs.
    Provides a search-then-fetch workflow for discovering and downloading
    data from national statistics agencies (SCB, SSB, Statistics Finland,
    etc.) using a consistent tibble-based interface.

Functions for import, export, visualization and other manipulations of bitmapped images.

Provides a 'tidyverse'-style interface to the Brazilian Central Bank (<https://www.bcb.gov.br>) 'PIX' Open Data 'API' <https://olinda.bcb.gov.br/olinda/servico/Pix_DadosAbertos/versao/v1/aplicacao#!/recursos>. 
    Retrieve statistics on 'PIX' keys, transactions by municipality, and monthly 
    transaction summaries. All functions return 'tibbles' and support 'OData' 
    query parameters for filtering, selecting, and ordering data.

An implementation of chunked, compressed,
  N-dimensional arrays for R. 'Zarr' spec V2 (2024)
  <doi:10.5281/zenodo.11320255>.

Computes the Patient-Reported Outcomes (PROs) Joint Contrast (PJC), 
 a residual-based summary that captures information left over after accounting 
 for the clinical Disease Activity index for Psoriatic Arthritis (cDAPSA). 
 PROs (pain and patient global assessment) and joint counts (swollen and tender) 
 are standardized, then each component is adjusted for standardized cDAPSA using 
 natural spline coefficients that were derived from previously published models. 
 The resulting residuals are standardized and combined using fixed principal 
 component loadings, to yield a continuous PJC score and quartile groupings. 
 This package provides a calculator for applying those published coefficients to new datasets; 
 it does not itself estimate spline models or principal components.

Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events
    and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. 
    Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. 
    Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.