Spatially explicit population genetic simulations
This package creates a individual-based population genetic
simulation.  Individuals have spatial coordiantes, dispersal
governed by mixtures of Weibull and normal pdfs.  It is
discrete-time.  It can be arbitrarily complex given enough
known or assumed demographic characteristics.  Simulates
diploid and haploid inheritance. Allows the development of null
distributions of genotypes for complex demographic scenarios.
This version includes some approximations for pollen (male
gamete) dispersal.

This package creates a individual-based population genetic simulation.  Individuals have spatial coordinates, dispersal governed by mixtures of Weibull and normal pdfs.  It is discrete-time.  It can be arbitrarily complex given enough known or assumed demographic characteristics.  Simulates diploid and haploid inheritance. Allows the development of null distributions of genotypes for complex demographic scenarios.  This version includes some approximations for pollen (male gamete) dispersal.

This package creates a individual-based population genetic simulation.  Individuals have spatial coordiantes, dispersal governed by mixtures of Weibull and normal pdfs.  It is discrete-time.  It can be arbitrarily complex given enough known or assumed demographic characteristics.  Simulates diploid and haploid inheritance. Allows the development of null distributions of genotypes for complex demographic scenarios.

Statistical Learning with Kernel for Persistence Diagrams
c(
person("Tullia", "Padellini", email = "tullia.padellini@uniroma1.it", role = c("aut", "cre")),
person("Francesco", "Palini", role = "aut"),
person("Pierpaolo", "Brutti", role = "ctb"),
person("David", "Meyer", role = c("ctb","cph"), comment = "libsvm to R code (e1071 package)"),
person("Chih-Chung", "Chang", role = c("ctb","cph"), comment = "LIBSVM C++ code"),
person("Chih-Chen", "Lin", role = c("ctb","cph"), comment = "LIBSVM C++ code"),
person("Michael","Kerber", role = c("ctb", "cph"), comment = "HERA C++ code"),
person("Dmitriy","Morozov", role = c("ctb", "cph"), comment = "HERA C++ code"),
person("Arnur","Nigmetov", role = c("ctb", "cph"), comment = "HERA C++ code"))
Provides tools for exploiting topological information into standard statistical learning algorithms.
To this aim, this package contains the most popular kernels defined on the space of persistence diagrams, and persistence images.
Moreover, it provides a solver for kernel Support Vector Machines problems, whose kernels are not necessarily positive semidefinite,
based on the C++ library 'LIBSVM' <https://www.csie.ntu.edu.tw/~cjlin/libsvm/>, and on its R implementation 'e1071'.
Additionally, it allows to compute Wasserstein distance between persistence diagrams with an arbitrary ground metric,
building an R interface for the C++ library 'HERA' <https://bitbucket.org/grey_narn/hera/src/master/>.
Rcpp (>= 1.0.1), mvtnorm, Rdpack, methods, stats
TDA, knitr, rmarkdown, SparseM, Matrix, kernlab, viridis
Tullia Padellini [aut, cre],
Francesco Palini [aut],
Pierpaolo Brutti [ctb],
David Meyer [ctb, cph] (libsvm to R code (e1071 package)),
Chih-Chung Chang [ctb, cph] (LIBSVM C++ code),
Chih-Chen Lin [ctb, cph] (LIBSVM C++ code),
Michael Kerber [ctb, cph] (HERA C++ code),
Dmitriy Morozov [ctb, cph] (HERA C++ code),
Arnur Nigmetov [ctb, cph] (HERA C++ code)