Electronic health records (EHR) linked with biorepositories are 
    a powerful platform for translational studies. A major bottleneck exists 
    in the ability to phenotype patients accurately and efficiently. 
    Towards that end, we developed an automated high-throughput 
    phenotyping method integrating International 
    Classification of Diseases (ICD) codes and narrative data extracted 
    using natural language processing (NLP). Specifically, our proposed method, 
    called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent 
    mixture models on aggregated ICD and NLP counts along with healthcare 
    utilization. The MAP algorithm yields a predicted probability of phenotype 
    for each patient and a threshold for classifying subjects with phenotype 
    yes/no (See Katherine P. Liao, et al. (2019) <doi:10.1093/jamia/ocz066>.).

Allows the user to generate a list of features (gene, pseudo, RNA,
    CDS, and/or UTR) directly from NCBI database for any species with a current 
    build available. Option to save downloaded and formatted files is available, 
    and the user can prioritize the feature list based on type and assembly builds 
    present in the current build used. The user can then use the list of features 
    generated or provide a list to map a set of markers (designed for SNP markers 
    with a single base pair position available) to the closest feature based on 
    the map build. This function does require map positions of the markers to be 
    provided and the positions should be based on the build being queried through 
    NCBI.

Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.