Maintainer: Nathan S. Watson-Haigh <nathan.watson-haigh@awri.com.au>

Maintainer: Nathan S. Watson-Haigh <nathan.haigh@acpfg.com.au>

Title: Partial Correlation Coefficient with Information Theory
Description: Apply Partial Correlation coefficient with Information
To reduce compute time by making use of multiple compute cores,
simply run PCIT on a computer with has multiple cores and also
has the Rmpi package installed. PCIT will then auto-detect the
multicore environment and run in parallel mode without the need
to rewrite your scripts. This makes scripts, using PCIT, portable
across single core (or no Rmpi package installed) computers
which will run in serial mode and multicore (with Rmpi package
installed) computers which will run in parallel mode.

Description: We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 <arXiv:2009.10982>.
Packaged: 2021-04-09 00:59:22 UTC; andrewying

Title: Group Regression Models for Risk Protein Complex Identification
person("Wei", "Liu", , "freelw@qq.com", role = c("aut", "cre"),
Description: Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.

Title: Simulation and Likelihood Calculation of Phylogenetic
Authors@R: c(person("Venelin", "Mitov", email = "vmitov@gmail.com",
Description: Phylogenetic comparative methods represent models of continuous trait
data associated with the tips of a phylogenetic tree. Examples of such models
are Gaussian continuous time branching stochastic processes such as Brownian
motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the
tips of the tree as an observed (final) state of a Markov process starting from
an initial state at the root and evolving along the branches of the tree. The
PCMBase R package provides a general framework for manipulating such models.
This framework consists of an application programming interface for specifying
data and model parameters, and efficient algorithms for simulating trait evolution
under a model and calculating the likelihood of model parameters for an assumed
model and trait data. The package implements a growing collection of models,
which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed
Gaussian models, in which different types of the above models can be associated
with different branches of the tree. The PCMBase package is limited to
trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic
models. The PCMFit package provides functionality for ML and Bayesian fit of
these models to tree and trait data. The package web-site
provides access to the documentation and other resources.
Imports: ape, expm, mvtnorm, data.table, ggplot2, xtable
Suggests: testthat, knitr, rmarkdown, abind, ggtree, cowplot, covr,