Provides utility functions used by the Genome Analysis Toolkit ('GATK') to load tables and plot data. The 'GATK' is a toolkit for variant discovery in high-throughput sequencing data.

Two functions for Generalized SARIMA time series simulation
Write SARIMA models in (finite) AR representation and simulate
generalized multiplicative seasonal autoregressive moving average (time) series
with Normal / Gaussian, Poisson or negative binomial distribution.

Write SARIMA models in (finite) AR representation and
simulate generalized multiplicative seasonal autoregressive
moving average (time) series with Normal / Gaussian, Poisson or
negative binomial distribution.

Two Functions for Generalized SARIMA Time Series Simulation
Write SARIMA models in (finite) AR representation and simulate
generalized multiplicative seasonal autoregressive moving average (time) series
with Normal / Gaussian, Poisson or negative binomial distribution.
The methodology of this method is described in Briet OJT, Amerasinghe PH, and
Vounatsou P (2013) <doi:10.1371/journal.pone.0065761>.

Group Sequential Operating Characteristics for Clinical,
Bayesian two-arm Trials with known Sigma and Normal Endpoints.

c(person("Florian", "Gerber", role = c("aut")),
person("Thomas", "Gsponer", role = "aut"),
person("Bjoern", "Bornkamp", role = "cre",
email = "bbnkmp@mail.de",
comment = "maintainer"))
Group Sequential Operating Characteristics for Clinical,
Bayesian two-arm Trials with known Sigma and Normal Endpoints,
as described in Gerber and Gsponer (2016) <doi: 10.18637/jss.v069.i11>.
Florian Gerber [aut],
Thomas Gsponer [aut],
Bjoern Bornkamp [cre] (maintainer)

Estimate Parameters in the Generalized SBM
c(person("Genevieve", "Robin", role = c("aut"), email = "genevieve.robin@inria.fr"),
person("Solenne", "Gaucher", role = c("aut", "cre"), email = "solenne.gaucher@math.u-psud.fr"))
Given an adjacency matrix drawn from a Generalized Stochatsic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes, as describes in Gaucher, Klopp and Robin (2019) <arXiv:1911.13122>.
knitr, rmarkdown, igraph, missSBM, RColorBrewer, Matrix
Genevieve Robin [aut],
Solenne Gaucher [aut, cre]