Packaged: 2019-09-13 15:50:16 UTC; michael_science

BiocGenerics (>= 0.20.0), Biostrings (>= 2.48.0),
Packaged: 2020-02-08 20:49:06 UTC; michael_science

Packaged: 2020-02-20 19:19:56 UTC; michael_science

GenomicAlignments (>= 1.18.1), GenomicRanges, IRanges
Description: Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2020) <doi:10.1186/s12859-020-3475-0>.
Packaged: 2020-11-21 22:48:47 UTC; michael_science

Title: Nonparametric Estimation of Preferential Attachment and Node
Author: Thong Pham, Paul Sheridan, Hidetoshi Shimodaira
Description: A statistically sound method for estimating jointly the attachment function and node fitness in a temporal complex network by maximizing a suitable penalized log-likelihood function is implemented in this package.

Description: A statistically sound method for estimating jointly the attachment function and node fitness in a temporal complex network by maximizing a suitable penalized log-likelihood function is implemented in this package. Tutorial can be found in the accompanying vignette. More details about the method can be found at http://dx.doi.org/10.1371/journal.pone.0137796. To cite this package in your projects, please run the command: citation("PAFit") for a list of references you should cite.
Imports: Rcpp (>= 0.11.3) , grDevices, graphics, stats

Description: A statistically sound method for estimating jointly the attachment function and node fitness in a temporal complex network by maximizing a suitable penalized log-likelihood function is implemented in this package. Tutorial can be found in the accompanying vignette. For a list of references, please run the command: citation("PAFit").
Imports: Rcpp (>= 0.11.3) , grDevices, graphics, stats, RColorBrewer,

Title: Joint Inference of Preferential Attachment and Node Fitness in
Description: A framework for modelling and inferencing the attachment mechanisms of temporal complex networks is implemented in this package. For estimating the preferential attachment (PA) function in isolation, we implement Jeong's method, the corrected Newman's method and the PAFit method. For jointly estimating the PA function and node fitnesses, we implement the PAFit method. The package also provides flexible methods to generate a wide range of temporal networks based on PA and fitness.