Estimates life tables, specifically (crude) death rates and (raw and graduated) death probabilities, using rolling windows in one (e.g., age), two (e.g., age and time) or three (e.g., age, time and income) dimensions. The package can also be utilised for summarising statistics and smoothing continuous variables through rolling windows in other domains, such as estimating averages of self-positioning ideology in political science.
   Acknowledgements:
   The authors wish to thank Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Generalitat Valenciana (grants HIECPU/2023/2, Conselleria de Hacienda, Economía y Administración Pública, and CIGE/2023/7, Conselleria de Educación, Cultura, Universidades y Empleo) for supporting this research.

Contains convenience functions for working with spatial data across
    multiple UTM zones, raster-vector operations common in the analysis of 
    conflict data, and converting degrees, minutes, and seconds latitude and
    longitude coordinates to decimal degrees.

Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henríquez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.

An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.