Calculates Land Surface Temperature from Landsat band 10 and 11.
  Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). <doi: 10.1109/TGRS.2008.2007125>.
  Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). <doi:10.1016/j.rse.2004.02.003>.
  Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). <doi: 10.1016/j.asr.2009.01.023>.
  Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). <doi:10.1016/0034-4257(96)00039-9>.
  On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). <doi:10.1080/01431169308904400>.
  Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). <doi:10.3390/rs6109829>.
  Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. Skoković D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014).

"Lessons in Statistical Thinking" D.T. Kaplan (2014) 
    <https://dtkaplan.github.io/Lessons-in-statistical-thinking/> 
    is a textbook for a first or second course in statistics that embraces 
    data wrangling, causal reasoning, modeling, statistical adjustment, 
    and simulation. 'LSTbook' supports the student-centered, tidy, 
    pipeline-oriented computing style featured in the book.

A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network, 
             which is originally developed by 
             Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided. 
             The sample size of the dataset used to train the LSTM model is 1,000,000. 
             Each sample is a batch of simulated response data with a specific latent factor structure. 
             The eigenvalues of these response data will be used as sequential data to train the LSTM. 
             The pre-trained LSTM is capable of factor retention for real response data with a 
             true latent factor number ranging from 1 to 10, that is, determining the number of factors.