The Fill-Mask Association Test ('FMAT')
    is an integrative and probability-based method using
    Masked Language Models to measure conceptual associations
    (e.g., attitudes, biases, stereotypes, social norms, cultural values)
    as propositions in natural language.
    Supported language models include 'BERT'
    <doi:10.48550/arXiv.1810.04805> and its variants available at 'Hugging Face'
    <https://huggingface.co/models?pipeline_tag=fill-mask>.
    Methodological references and installation guidance are provided at
    <https://psychbruce.github.io/FMAT/>.

Implements basic financial market objects like currencies, currency
  pairs, interest rates and interest rate indices. You will be able to use
  Benchmark instances of these objects which have been defined using their most
  common conventions or those defined by International Swap Dealer Association
  (ISDA, <https://www.isda.org>) legal documentation.

Generate cost effective minimally changed run sequences 
              for symmetrical as well as asymmetrical factorial 
              designs.

Current status data abounds
            in the field of epidemiology and public health, where the only observable data for a subject is
            the random inspection time and the event status at inspection. Motivated by such a current status
            data from a periodontal study where data are inherently clustered,
            we propose a unified methodology to analyze such complex data.

Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC)
         framework for implementing Metropolis-Hastings algorithm in a modular way
         allowing users to specify automatic convergence checker, personalized
         transition kernels, and out-of-the-box multiple MCMC chains using
         parallel computing. Most of the methods implemented in this package can
         be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods
         included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5>
         Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5>
         Robust Adaptive Metropolis, and Thawornwattana et
         al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.