Performs structured OLS (sOLS) and structured SIR (sSIR).

Species sensitivity distributions are cumulative probability
    distributions which are fitted to toxicity concentrations for
    different species as described by Posthuma et al.(2001)
    <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to
    fit distributions such as the gamma, log-logistic, log-normal and
    log-normal log-normal mixture. Multiple distributions can be averaged
    using Akaike Information Criteria.  Confidence intervals on hazard
    concentrations and proportions are produced by bootstrapping.

Calculates a modified Simplified Surface Energy Balance Index (SSEBI) and the Evaporative Fraction (EF) using geospatial raster data such as albedo and surface-air temperature difference (TS–TA). The SSEBI is computed from albedo and TS–TA to estimate surface moisture and evaporative dynamics, providing a robust assessment of surface dryness while accounting for atmospheric variations. Based on Roerink, Su, and Menenti (2000) <doi:10.1016/S1464-1909(99)00128-8>.

Weighted-ensemble regression over base learners supported by
    'caret' (Kuhn (2008) <doi:10.18637/jss.v028.i05>), with cross-validated
    hyperparameter selection, out-of-fold diagnostics, and signed residual-
    offset estimates. Multi-response problems use iterative input-space
    expansion related to Spyromitros-Xioufis et al. (2016)
    <doi:10.1007/s10994-016-5546-z>, with Jacobi or 'Gauss-Seidel' sweeps,
    package-defined companion gates and per-response iteration stitching.
    A package-defined pseudo-label stage promotes prediction rows by a
    cross-model and cross-dataset range ratio and accepts rounds with an
    out-of-fold squared-correlation gauge.

Functionality to parse server-sent events with a high-level
    interface that can be extended for custom applications.

Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.