Showing 4 of total 4 results (show query)
ineswilms
bigtime:Sparse Estimation of Large Time Series Models
Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.
Maintained by Ines Wilms. Last updated 2 years ago.
30 stars 4.94 score 29 scripts
lance-waller-lab
envi:Environmental Interpolation using Spatial Kernel Density Estimation
Estimates an ecological niche using occurrence data, covariates, and kernel density-based estimation methods. For a single species with presence and absence data, the 'envi' package uses the spatial relative risk function that is estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Maintained by Ian D. Buller. Last updated 5 months ago.
ecological-nicheecological-niche-modellinggeospatialgeospatial-analysiskernel-density-estimationniche-modelingniche-modellingnon-euclidean-spacespoint-patternpoint-pattern-analysisprincipal-component-analysisspatial-analysisspecies-distribution-modelingspecies-distribution-modelling
1 stars 4.22 score 33 scripts
amishra-stats
robregcc:Robust Regression with Compositional Covariates
We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.
Maintained by Aditya Mishra. Last updated 4 years ago.
6 stars 4.11 score 43 scripts
druegamer
deepregression:Fitting Deep Distributional Regression
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>. Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
Maintained by David Ruegamer. Last updated 4 months ago.
2.28 score 63 scripts 1 dependents