A set of functions to calculate solar irradiance and insolation on Mars horizontal and inclined surfaces. Based on NASA Technical Memoranda 102299, 103623, 105216, 106321, and 106700, i.e. the canonical Mars solar radiation papers.

The MARSS package provides maximum-likelihood parameter
    estimation for constrained and unconstrained linear multivariate autoregressive
    state-space (MARSS) models, including partially deterministic models. MARSS models are a class 
    of dynamic linear model (DLM) and vector autoregressive model (VAR)
    model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB'
    (using the 'marssTMB' companion package). Functions are provided for parametric and 
    innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including
    bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all
    conditional residual types. See the user guide for examples of dynamic factor analysis, 
    dynamic linear models, outlier and shock detection, and multivariate AR-p models.  
    Online workshops (lectures, eBook, and computer labs) 
    at <https://atsa-es.github.io/>.

Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.

A way to estimate and test marginal mediation effects for 
             zero-inflated compositional mediators. Estimates of Natural Indirect Effect (NIE),
             Natural Direct Effect (NDE) of each taxon, as well as their standard errors and 
             confident intervals, were provided as outputs. Zeros will not be imputed during 
             analysis. See Wu et al. (2022) <doi:10.3390/genes13061049>.

Mixed model-based genome-wide association analysis that accommodate population membership information, variance adjustment, and correlated responses.