Package index • xnicher

All functions

AIC(<xnicher>): Akaike information criterion for a xnicher fit
BIC(<xnicher>): Bayesian information criterion for a xnicher fit
assess(): Assess acceptance criteria for an optimization result
assess(<xnicher>): Assess acceptance criteria for a xnicher result
compare_xnicher(): Compare xnicher fits side-by-side via AIC and BIC
cv_xnicher(): k-fold (or leave-one-out) cross-validation for a xnicher fit
cvine_cholesky(): Build Cholesky factor of a correlation matrix from C‑vine partial correlations
example_env_m_2d: Samples of environmental data from M hypothesis to estimate negative log likelihood from Abeillia abeillei presence points. This is a hummingbird example. A dataset with two variables containing points contains 2 bioclimatic variables
example_env_m_3d: Samples points from M hypothesis to estimate negative log likelihood from Abeillia abeillei presence points. This is a hummingbird example. A dataset with three columns containing extracted information from 3 bioclimatic variables
example_env_occ_2d: Species occurrence points to estimate negative log likelihood from Abeillia abeillei presence points. This is a hummingbird example. A dataset with two variables containing points contains 2 bioclimatic variables
example_env_occ_3d: Species occurrence points to estimate negative log likelihood from Abeillia abeillei presence points. This is a hummingbird example. A dataset with three variables containing points contains 3 bioclimatic variables
example_mu_vec: Example of a vector of the center of an ellipsoid from two environmental variables. vector of length 2 corresponding to the centroid of an ellipsoid
example_occ_df: Species occurrence points from Abeillia abeillei presence points after download and clean from GBIF. This is a hummingbird example. A dataset with three variables. Contains scientific name, longitude and latitude.
example_s_mat: Example of a covariance matrix of an ellipsoid from two environmental variables. The 2 x 2 matrix corresponded to a positive semi-definite matrix. In two dimensions encodes the rotation (orientation) and scaling of an ellipse.
example_vicugna: Samples of environmental data from M hypothesis to estimate negative log likelihood from Vicugna vicugna.
geom_xnicher_background(): Background environment layer
geom_xnicher_ellipse(): Niche-ellipse layer derived from a fitted xnicher model
geom_xnicher_isosuitability(): Iso-suitability contour layer derived from a fitted xnicher model
geom_xnicher_occ(): Occurrence-points layer
get_ellipsoid_pars(): Get ellipsoid parameters. A function to compute average and the inverse of covariance matrix from environmental data
habitat_suitability(): Tiled habitat-suitability map from an environmental terra stack
kde_gaussian(): Multivariate Gaussian KDE with fixed Scott bandwidth
logLik(<xnicher>): Log-likelihood of a fitted xnicher model
loglik_niche(): Negative log likelihood of an ellipsoid corrected with environmental combinations which come from the area of study (M)
loglik_niche_math_cpp(): Negative log-likelihood (M-restricted, math scale, Cholesky version)
loglik_niche_math_ip_weighted(): Negative log-likelihood (inverse-probability-weighted normal, math scale)
loglik_niche_math_ip_weighted_integrated(): Negative log-likelihood (inverse-probability-weighted normal, integrated C++)
loglik_niche_math_presence_only(): Negative log-likelihood (presence-only, math scale)
niche_ip_weighted(): Fit inverse-probability-weighted (IPW) normal niche model
niche_presence_only(): Fit presence-only Gaussian niche model
nobs(<xnicher>): Number of observations used to fit a xnicher model
optimize_niche(): Optimize niche model log-likelihood with multi-start Sobol design
optimize_niche_xptr(): Optimize niche model using a compiled XPtr backend
predict(<xnicher>): Habitat-suitability raster from a fitted xnicher object
print(<xnicher>): Print a xnicher object
rhipicephalus: Rhipicephalus microplus occurrence and background environment data
rhipicephalus_occ: Rhipicephalus microplus occurrence coordinates
start_theta(): Starting values for niche model on math scale
start_theta_multiple(): Generate multiple starting points for niche model optimization