Provide methods for estimating optimal treatment regimes in survival contexts 
    with Kaplan-Meier-like estimators when no unmeasured confounding assumption is 
    satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) <doi:10.1111/rssb.12201>) 
    and when no unmeasured confounding assumption fails to hold and a binary instrument 
    is available (Xia, J., Zhan, Z., Zhang, J. (2022) <arXiv:2210.05538>).

A penalized regression framework that can simultaneously estimate 
    the optimal treatment strategy and identify important variables. 
    Appropriate for either censored or uncensored continuous response.

An implementation of several functions for feature extraction in 
    ordinal time series datasets. Specifically, some of the features proposed by
    Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed.  
    These features can be used to perform inferential tasks or to feed machine
    learning algorithms for ordinal time series, among others. The package also includes some
    interesting datasets containing financial time series. Practitioners from a 
    broad variety of fields could benefit from the general framework provided 
    by 'otsfeatures'.

Tools to segment fire scars and assess severity and vegetation regeneration using 
    'Otsu' thresholding on Relative Burn Ratio (RBR) and differenced Normalized Burn Ratio (dNBR) image composites. 
    Includes support for mosaic handling, polygon metrics, post-fire regeneration detection, day-of-year flagging, 
    and validation against reference datasets. Designed for analysis of fire history in the Iberian Peninsula. 
    Input Landsat composites follow the methodology described in Quintero et al. (2025) <doi:10.2139/ssrn.4929831>.

Provides tools to process raster data and apply Otsu-based thresholding for burned area mapping and other image segmentation tasks. Implements the method described by Otsu (1979) <doi:10.1109/TSMC.1979.4310076>, a data-driven technique that determines an optimal threshold by maximizing the inter-class variance of pixel intensities. It includes validation functions to assess segmentation accuracy against reference data using standard accuracy metrics such as precision, recall, and F1-score.