Measures real distances in pictures. With PDM() function, you can choose one '*.jpg' file, select the measure in mm of scale, starting and and finishing point in the graphical scale, the name of the measure, and starting and and finishing point of the measures. After, ask the user for a new measure.

Fits heterogeneous panel data models with interactive effects for linear regression, logistic, count, probit, quantile, and clustering. Based on Ando, T. and Bai, J. (2015) "A simple new test for slope homogeneity in panel data models with interactive effects" <doi: 10.1016/j.econlet.2015.09.019>, Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" <doi: 10.1093/jjfinex/nbu026> , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" <doi: 10.1002/jae.2467>, Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" <doi: 10.1080/01621459.2016.1195743>, Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" <doi: 10.1080/01621459.2018.1543598>, Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" <doi: 10.1016/j.jeconom.2020.11.013.>.

Fits a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.

Building patient level networks for prediction of 
        medical outcomes and draw the cluster of network.        
        This package is based on paper Personalized disease 
        networks for understanding and predicting cardiovascular 
        diseases and other complex processes 
        (See Cabrera et al. <http://circ.ahajournals.org/content/134/Suppl_1/A14957>).