SystemRequirements: CUDA >= v7.0, Nvidia GPU (e.g. GeForce or Tesla)
Packaged: 2016-10-17 07:53:46 UTC; Sven Koitka

Packaged: 2016-05-30 09:12:51 UTC; Sven Koitka

Title: Goodness of Fit Checks for Binomial N-Mixture Models
Authors@R: person("Jonas", "Knape", email = "jonas.knape@slu.se", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8012-5131"))
Description: Provides residuals and overdispersion metrics to assess the fit of N-mixture models obtained using the package 'unmarked'.
Details on the methods are given in Knape et al. (2017) <doi:10.1101/194340>.
Author: Jonas Knape [aut, cre] (<https://orcid.org/0000-0002-8012-5131>)

person("Simona","Jokubauskaite", email = "rteam.prog@gmail.com", role=c("aut", "cre")),
Description: Estimates a subset of nonlinear multivariate models (NMM):
system of nonlinear regressions (SNR), logit, and a joint model of SNR and logit.
'nmm' uniquely accounts for correlations between the error terms from nonlinear regressions
and the probabilities from logit models.
It also enables a very flexible design of logit: alternative-specific indirect utilities,
individual-specific choice set and number of actual choices.
Imports: Rdpack, AER, mlogit, Hmisc, stats, gsubfn, abind, tidyr, plyr,
Depends: R (>= 4.0), systemfit, DEoptim, data.table, magrittr, maxLik
Suggests: knitr, rmarkdown, recipes, Stat2Data
Author: Simona Jokubauskaite [aut, cre],

Title: Non Metric Space (Approximate) Library
Authors@R: c( person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre")), person("B.", "Naidan", role = "cph", comment = "Author of the Non-Metric Space Library (NMSLIB)"), person("L.", "Boytsov", role = "cph", comment = "Author of the Non-Metric Space Library (NMSLIB)"), person("Yu.", "Malkov", role = "cph", comment = "Author of the Non-Metric Space Library (NMSLIB)"), person("B.", "Frederickson", role = "cph", comment = "Author of the Non-Metric Space Library (NMSLIB)"), person("D.", "Novak", role = "cph", comment = "Author of the Non-Metric Space Library (NMSLIB)") )
Description: A Non-Metric Space Library ('NMSLIB' <https://github.com/searchivarius/nmslib>) wrapper, which according to the authors "is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the 'NMSLIB' <https://github.com/searchivarius/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' <https://github.com/searchivarius/nmslib> 'Python' Library.
SystemRequirements: Python (>= 2.7), nmslib ( >= 1.7.1), scipy ( >=
1.0.0), numpy ( >= 1.14.0). Detailed installation instructions
Imports: Rcpp (>= 0.12.7), reticulate, R6, Matrix
B. Naidan [cph] (Author of the Non-Metric Space Library (NMSLIB)),
L. Boytsov [cph] (Author of the Non-Metric Space Library (NMSLIB)),
Yu. Malkov [cph] (Author of the Non-Metric Space Library (NMSLIB)),
B. Frederickson [cph] (Author of the Non-Metric Space Library (NMSLIB)),
D. Novak [cph] (Author of the Non-Metric Space Library (NMSLIB))