Showing 3 of total 3 results (show query)
covaruber
sommer:Solving Mixed Model Equations in R
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 4 days ago.
average-informationmixed-modelsrcpparmadilloopenblascppopenmp
44 stars 12.63 score 300 scripts 10 dependents
cran
sn:The Skew-Normal and Related Distributions Such as the Skew-t and the SUN
Build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t and the SUN families. For the skew-normal and the skew-t distributions, statistical methods are provided for data fitting and model diagnostics, in the univariate and the multivariate case.
Maintained by Adelchi Azzalini. Last updated 2 years ago.
3 stars 7.37 score 91 dependents
lehmasve
hdflex:High-Dimensional Aggregate Density Forecasts
Provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2023) <doi:10.2139/ssrn.4342487>.
Maintained by Sven Lehmann. Last updated 5 months ago.
ensemble-learningforecast-combinationforecastinghigh-dimensionalitytime-seriesopenblascppopenmp
3 stars 3.78 score 1 scripts