It is a hybrid spatial model that combines the strength of two widely used regression models, MARS (Multivariate Adaptive Regression Splines) and
             GWR (Geographically Weighted Regression) to provide an effective approach for predicting a response variable at unknown locations. The MARS model
             is used in the first step of the development of a hybrid model to identify the most important predictor variables that assist in predicting the response
             variable. For method details see, Friedman, J.H. (1991). <DOI:10.1214/aos/1176347963>.The GWR model is then used to predict the response variable at 
             testing locations based on these selected variables that account for spatial variations in the relationships between the variables. This hybrid model 
             can improve the accuracy of the predictions compared to using an individual model alone.This developed hybrid spatial model can be useful particularly in 
             cases where the relationship between the response variable and predictor variables is complex and non-linear, and varies across locations.

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/>.