(>= 1.5-9), plotrix (>= 3.5-12), parallel, methods, ellipse,
Suggests: RGtk2 (>= 2.20.31), knitr, rmarkdown, HSAUR3, tidyr(>=

Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.

Packaged: 2022-01-18 10:21:25 UTC; sfriedrich

Author: Jiehuan Sun [aut, cre], Katherine P. Liao[aut], Sheng Yu [aut], Tianxi Cai [aut]
Description: 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
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
Packaged: 2019-03-29 19:47:25 UTC; JiehuanSun

Title: Multiple Aggregation Prediction Algorithm
Author: Nikolaos Kourentzes and Fotios Petropoulos
Description: This package contains functions and wrappers for using the Multiple Aggregation Prediction Algorithm, proposed by Kourentzes et al. (2014).
Depends: forecast (>= 5.3), miscTools, parallel

Description: 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.

Description: This package contains 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.