User-friendly and generalized tools for the calculation of luck -- moments of variation in metrics like lifespan and lifetime reproductive output. We provide tools for calculating those moments and also performing decompositions into contributions from, for example, individual traits, environmental impacts, and luck (also called individual stochasticity). The functions included here are based on Snyder and Ellner (2024) <doi:10.1086/730557>, Cochran and Ellner (1992) <https://www.jstor.org/stable/2937115>, and Hernandez et al. (2024) <doi:10.1111/ele.14390>.

Probabilistic record linkage without direct identifiers using only 
    diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, 
    Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ;
    Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane 
    & Cai (2021) <doi: 10.1093/jamia/ocab187>.

Contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.

Miscellaneous functions commonly used by LuLab. This package aims to help more researchers on epidemiology to perform data management and visualization more efficiently.

Classes and methods for spatially explicit land use change
    modelling in R.

A framework that allows for easy logging of changes in data.
    Main features: start tracking changes by adding a single line of code to 
    an existing script. Track changes in multiple datasets, using multiple 
    loggers. Add custom-built loggers or use loggers offered by other 
    packages. <doi:10.18637/jss.v098.i01>.

Implements a fast and resistant divisive clustering algorithm which
    identifies a specified number of clusters:  'lumbermark' iteratively
    chops off sizeable limbs that are joined by protruding segments
    of a dataset's mutual reachability minimum spanning tree;
    see Gagolewski (2026) <https://lumbermark.gagolewski.com/>.
    The use of a mutual reachability distance pulls peripheral points farther
    away from each other.  When combined with the 'deadwood' package, it can
    act as an outlier detector.  The 'Python' version of 'lumbermark' is
    available via 'PyPI'.