Facilitates basic spatial edge correction to point pattern data.

Datasets from most recent CCIIO DIY entry
  in a tidy format. These support the Centers for Medicare and Medicaid
  Services' (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows
  health insurance issuers to calculate member risk profiles under the Health
  and Human Services-Hierarchical Condition Categories (HHS-HCC) regression
  model. This regression model is used to calculate risk adjustment transfers.
  Risk adjustment is a selection mitigation program implemented under the
  Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA.
  Under the ACA, health insurance issuers submit claims data to CMS	in order
  for CMS to calculate a risk score under the HHS-HCC regression model.
  However, CMS does not inform issuers of their average risk score until after
  the data submission deadline. These data sets can be used by issuers to
  calculate their average risk score mid-year. More information about risk
  adjustment and the HHS-HCC model can be found here:
  <https://www.cms.gov/mmrr/Articles/A2014/MMRR2014_004_03_a03.html>.

A system to facilitate designing comparative (and non-comparative) experiments using the 
  grammar of experimental designs <https://emitanaka.org/edibble-book/>. 
  An experimental design is treated as an intermediate, mutable object that is 
  built progressively by fundamental experimental components like units, treatments, and their relation.
  The system aids in experimental planning, management and workflow.

The purpose of this package is to estimate the potential of urban agriculture to contribute to addressing several urban challenges at the city-scale. Within this aim, we selected 8 indicators directly related to one or several urban challenges. Also, a function is provided to compute new scenarios of urban agriculture. Methods are described by Pueyo-Ros, Comas & Corominas (2023) <doi:10.12688/openreseurope.16054.1>.

Perform a Bayesian estimation of the exploratory 
    deterministic input, noisy and gate (EDINA)
    cognitive diagnostic model described by Chen et al. (2018)
    <doi:10.1007/s11336-017-9579-4>.