Best Orthogonalized Subset Selection (BOSS) is a least-squares (LS) based subset selection method, that performs best subset selection upon an orthogonalized basis of ordered predictors, with the computational effort of a single ordinary LS fit. This package provides a highly optimized implementation of BOSS and estimates a heuristic degrees of freedom for BOSS, which can be plugged into an information criterion (IC) such as AICc in order to select the subset from candidates. It provides various choices of IC, including AIC, BIC, AICc, Cp and GCV. It also implements the forward stepwise selection (FS) with no additional computational cost, where the subset of FS is selected via cross-validation (CV). CV is also an option for BOSS. For details see: Tian, Hurvich and Simonoff (2021), "On the Use of Information Criteria for Subset Selection in Least Squares Regression", <arXiv:1911.10191>.

Fork-safe, raw access to the 'Amazon Web Services' ('AWS') 'SDK' via the 'boto3' 'Python' module, and convenient helper functions to query the 'Simple Storage Service' ('S3') and 'Key Management Service' ('KMS'), partial support for 'IAM', the 'Systems Manager Parameter Store' and 'Secrets Manager'.

Analysis workflow for finding geographic boundaries of ecological or landscape traits and comparing the placement of geographic boundaries of two traits. If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) <doi:10.2307/3545584>.

Computes uniform bounds on the distance between
  the cumulative distribution function of a standardized sum
  of random variables and its first-order Edgeworth expansion,
  following the article Derumigny, Girard, Guyonvarch (2021)
  <arXiv:2101.05780>.

Generate ground truth cases for object localization algorithms. 
    Cycle through a list of images, select points around which to generate bounding 
    boxes and assign classifiers. Output the coordinates, and images annotated with 
    boxes and labels. For an example study that uses bounding boxes for image 
    localization and classification see Ibrahim, Badr, Abdallah, and Eissa (2012)
    "Bounding Box Object Localization Based on Image Superpixelization"
    <doi:10.1016/j.procs.2012.09.119>.