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).

A lightweight interchange layer for single-cell and spatial omics
    data, built on the L-star model of labelled axes and typed fields over them,
    serialized to the 'Zarr' format. Provides bidirectional converters
    ("profiles") for 'Seurat', 'SingleCellExperiment', 'Conos', and 'pagoda2'
    objects, including collections of heterogeneous samples, via a shared C++
    core ('libstar') so the same store is readable from R, 'Python', and C++.

"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.