Provides methods to estimate serial intervals and time-varying case
  reproduction numbers from infectious disease outbreak data. Serial intervals measure
  the time between symptom onset in linked transmission pairs, while case reproduction 
  numbers quantify how many secondary cases each infected individual generates 
  over time. These parameters are essential for understanding transmission 
  dynamics, evaluating control measures, and informing public health responses. 
  The package implements the maximum likelihood framework from Vink et al. (2014) 
  <doi:10.1093/aje/kwu209> for serial interval estimation and the retrospective 
  method from Wallinga & Lipsitch (2007) <doi:10.1098/rspb.2006.3754> for 
  reproduction number estimation. Originally developed for scabies transmission 
  analysis but applicable to other infectious diseases including influenza, 
  COVID-19, and emerging pathogens. Designed for epidemiologists, public health 
  researchers, and infectious disease modelers working with outbreak surveillance 
  data.

Provides tools for multiple imputation of missing data in multilevel
 modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo',
 and several functions for visualization, data management and the analysis 
 of multiply imputed data sets.

Allows the estimation and downstream statistical analysis of the mitochondrial DNA Heteroplasmy calculated from single-cell datasets <https://github.com/ScialdoneLab/MitoHEAR/tree/master>.

Tools to perform analyses and combine results from
        multiple-imputation datasets.

Extract, transform and load MITRE standards.
    This package gives you an approach to cybersecurity data sets.
    All data sets are build on runtime downloading raw data from MITRE public services.
    MITRE <https://www.mitre.org/> is a government-funded research organization 
    based in Bedford and McLean. Current version includes most used standards as
    data frames. It also provide a list of nodes and edges with all relationships.