A light-weight object-oriented system with 'python'-like syntax which supports multiple inheritances and incorporates a 'python'-like method resolution order.

A set of functions for doing analysis of A/B split test data and web metrics in general.

Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" <https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.

Frequentist inference on adaptively generated data. The methods implemented are based on Zhan et al. (2021) <doi:10.48550/arXiv.2106.02029> and Hadad et al. (2021) <doi:10.48550/arXiv.1911.02768>. For illustration, several functions for simulating non-contextual and contextual adaptive experiments using Thompson sampling are also supplied.

Implements functions that calculate upper prediction 
  bounds on the false discovery proportion (FDP) in the list of discoveries 
  returned by competition-based setups, implementing Ebadi et al. (2022)
  <arXiv:2302.11837>. Such setups include target-decoy competition (TDC) 
  in computational mass spectrometry and the knockoff construction in linear 
  regression (note this package typically uses the terminology of TDC). Included 
  is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the 
  simultaneous standardized and uniform bands. Requires 
  pre-computed Monte Carlo statistics available at 
  <https://github.com/uni-Arya/fdpbandsdata>. This data can be downloaded by
  running the command 'devtools::install_github("uni-Arya/fdpbandsdata")' in R
  and restarting R after installation. The size of this data is roughly 81Mb.

Assigns standardized diagnoses using the Banff Classification
    (Category 1 to 6 diagnoses, including Acute and Chronic active T-cell
    mediated rejection as well as Active, Chronic active, and Chronic antibody
    mediated rejection). The main function considers a minimal dataset
    containing biopsies information in a specific format (described by a data
    dictionary), verifies its content and format (based on the data dictionary),
    assigns diagnoses, and creates a summary report. The package is developed on
    the reference guide to the Banff classification of renal allograft pathology
    Roufosse C, Simmonds N, Clahsen-van Groningen M, et al. A (2018) <doi:10.1097/TP.0000000000002366>.
    The full description of the Banff classification is available at <https://banfffoundation.org/>.