This tool fits a non-parametric Bayesian model called a "hierarchically coupled mixture model with local dependence (HCMM-LD)" to the original microdata in order to generate synthetic microdata for privacy protection. The non-parametric feature of the adopted model is useful for capturing the joint distribution of the original input data in a highly flexible manner, leading to the generation of synthetic data whose distributional features are similar to that of the input data. The package allows the original input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output. The method builds on the work of Murray and Reiter (2016) <doi:10.1080/01621459.2016.1174132>.

Decode raw SYNOP (surface synoptic observations) messages into 
    data frames, extracting data from Sections 0, 1, and 3, including temperature, 
    dew point, pressure, wind, clouds, and precipitation. Available functions to 
    download SYNOP messages from Ogimet <https://www.ogimet.com/> if needed. 
    The decoding logic follows the specifications defined in the World Meteorological 
    Organization (2019) "Manual on Codes, Volume I.1 (WMO-No. 306)".

Explore synesthesia
  consistency test data, calculate consistency scores, 
  and classify participant data as valid or invalid.

A set of functions for generating 'SPSS' syntax files from the R environment.

Draw syntenic relationships between genome assemblies.
    There are 3 functions which take a tab delimited file containing alignment data for 
    syntenic blocks between genomes to produce either a linear alignment plot, 
    an evolution highway style plot, or a painted ideogram representing syntenic relationships.
    There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format
    to the required data structure.

Implements the synthetic control group method for comparative case studies
 as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller
 (2010, 2011, 2014). The synthetic control method allows for effect estimation in
 settings where a single unit (a state, country, firm, etc.) is exposed to an event
 or intervention. It provides a data-driven procedure to construct synthetic control
 units based on a weighted combination of comparison units that approximates the
 characteristics of the unit that is exposed to the intervention. A combination of
 comparison units often provides a better comparison for the unit exposed to the
 intervention than any comparison unit alone.