Imports: nortest, survival, sjmisc, stringr, magrittr, ztable
Suggests: testthat, knitr, ggplot2, sjlabelled, rmarkdown
Packaged: 2021-01-08 00:10:35 UTC; cardiomoon

Imports: nortest, survival, sjmisc, stringr, magrittr, crayon
Suggests: testthat, knitr, ggplot2, sjlabelled, ztable, rmarkdown
Packaged: 2022-01-05 07:14:22 UTC; cardiomoon

Title: Basic astronomical calculations with R
Description: A collection of basic astronomical routines for R based on
"Practical astronomy with your calculator" by Peter

Description: A collection of basic astronomica}l routines for R based on "Practical astronomy with your calculator" by Peter Duffet-Smith.
Packaged: Tue Jan  1 21:33:29 2008; Lukke

Title: Mean Squared Out-of-Sample Error Projection
person("Chris", "Rohlfs", , "car2228@columbia.edu", role = c("aut", "cre"),
Description: Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>.  It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.
Author: Chris Rohlfs [aut, cre] (<https://orcid.org/0000-0001-7714-9231>)

Imports: splancs, spam, spatstat, sampling, dismo, rpart, tree, e1071,
earth, randomForest, PresenceAbsence, lattice, gtools, ranger,
Title: Species Distribution MOdeling with Pseudo-Absences
Authors@R: c(person("Maialen", "Iturbide", role = c("aut", "cre"), email = "miturbide@ifca.unican.es"),
person("Joaquin", "Bedia", role = "ctb"),
person("SantanderMetGroup", "http://meteo.unican.es", role = "ctb"))
Description: Tools for transferable species distribution modeling and pseudo-absence
data generation allowing the straightforward design of relatively complex experiments
with multiple factors affecting the uncertainty (variability) of SDM outputs
(pseudo-absence sample, climate projection, modeling algorithm, etc.), and the
quantification of the contribution of different factors to the final variability
following the method described in Deque el al. (2010) <doi:10.1007/s00382-011-1053-x>.
Multiple methods for pseudo-absence data generation can be applied, including the novel
Three-step method as described in Iturbide et al. (2015) <doi:10.1016/j.ecolmodel.2015.05.018>.
Additionally, a function for niche overlap calculation is provided, considering the metrics