c(
person("Christoph", "Heibl", email = "christoph.heibl@gmx.net",
role = c("aut", "cre")),
person("Natalie", "Cusimano", role = c("aut")),
person("Franz-Sebastian", "Krah", role = c("aut")))
Functions that wrap popular phylogenetic software for sequence
alignment, masking of sequence alignments, and estimation of phylogenies and
ancestral character states.
Christoph Heibl [aut, cre],
Natalie Cusimano [aut],
Franz-Sebastian Krah [aut]

Spatially Explicit Capture-Recapture by Inverse Prediction
graphics, grDevices, MASS, utils, parallel, stats, tools,
stringr, nlme, Rcpp (>= 0.12.14)
person("Murray", "Efford", email = "murray.efford@otago.ac.nz", role = c("aut","cre"), comment = c(ORCID = "0000-0001-5231-5184"))
Estimates the density of a spatially distributed animal population sampled with an array of passive detectors, such as traps. Models incorporating distance-dependent detection are fitted by simulation and inverse prediction as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.
Murray Efford [aut, cre] (<https://orcid.org/0000-0001-5231-5184>)

Estimates the density of a spatially distributed animal population
sampled with an array of passive detectors, such as traps. Models incorporating
distance-dependent detection are fitted by simulation and inverse prediction
as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.
person("Murray", "Efford",
email = "murray.efford@otago.ac.nz",
role = c("aut","cre"),
comment = c(ORCID = "0000-0001-5231-5184"))
graphics, grDevices, MASS, nlme, parallel, Rcpp (>= 1.0.8.3),
stats, stringr, tools, utils
FrF2, knitr, plot3D , rmarkdown, sf, testthat (>= 0.11.0)

Intuitionistic, Pythagorean, and Spherical Fuzzy Similarity
Measure
Rama Ranjan Panda and Naresh Kumar Nagwani
Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets.