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Interface R to MPFR - Multiple Precision Floating-Point Reliable
Arithmetic (via S4 classes and methods) for arbitrary precision floating point numbers, including transcendental ("special") functions. To this end, the package interfaces to the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library which itself is based on the 'GMP' (GNU Multiple Precision) Library.
Extended Model Formulas
Infrastructure for extended formulas with multiple parts on the
right-hand side and/or multiple responses on the left-hand side
(see
Phase I/II CRM Based Drug Combination Design
Implements the adaptive designs for integrated phase I/II trials of drug combinations via continual reassessment method (CRM) to evaluate toxicity and efficacy simultaneously for each enrolled patient cohort based on Bayesian inference. It supports patients assignment guidance in a single trial using current enrolled data, as well as conducting extensive simulation studies to evaluate operating characteristics before the trial starts. It includes various link functions such as empiric, one-parameter logistic, two-parameter logistic, and hyperbolic tangent, as well as considering multiple prior distributions of the parameters like normal distribution, gamma distribution and exponential distribution to accommodate diverse clinical scenarios. Method using Bayesian framework with empiric link function is described in: Wages and Conaway (2014)
Spatial Generalised Linear Mixed Models for Areal Unit Data
Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991,
One-to-One Feature Matching
Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. The goal is to label the feature vectors using each label exactly once so to produce the best match across datasets, e.g. by minimizing the variability within classes. Statistical solutions based on empirical loss functions and probabilistic modeling are provided. The 'Gurobi' software and its 'R' interface package are required for one of the package functions (match.2x()) and can be obtained at < https://www.gurobi.com/> (free academic license). For more details, refer to Degras (2022)
Iterative Pruning Population Admixture Inference Framework
A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020)
Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs
Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. In this implementation we supply a "wrapper" function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. This version calls lp_solve version 5.5.
Graph/Network Visualization
Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.
Comprehensive Research Synthesis Tools for Systematic Reviews and Meta-Analysis
Functionalities for facilitating systematic reviews, data
extractions, and meta-analyses. It includes a GUI (graphical user interface)
to help screen the abstracts and titles of bibliographic data; tools to assign
screening effort across multiple collaborators/reviewers and to assess inter-
reviewer reliability; tools to help automate the download and retrieval of
journal PDF articles from online databases; figure and image extractions
from PDFs; web scraping of citations; automated and manual data extraction
from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for
Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools
to fill gaps in incomplete or missing study parameters; generation of random
effects sizes for Hedges' d, log response ratio, odds ratio, and correlation
coefficients for Monte Carlo experiments; covariance equations for modelling
dependencies among multiple effect sizes (e.g., effect sizes with a common
control); and finally summaries that replicate analyses and outputs from
widely used but no longer updated meta-analysis software (i.e., metawin).
Funding for this package was supported by National Science Foundation (NSF)
grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016)
Facilitating systematic reviews, data extraction and meta-analysis with the
metagear package for R. Methods in Ecology and Evolution 7, 323-330
Easily Install and Load the 'Tidyverse'
The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step. Learn more about the 'tidyverse' at < https://www.tidyverse.org>.