Handling of vegetation data from different sources ( Turboveg 2.0 <https://www.synbiosys.alterra.nl/turboveg/>; the German national repository <https://www.vegetweb.de> and others. Taxonomic harmonization (given appropriate taxonomic lists, e.g. the German taxonomic standard list "GermanSL", <https://germansl.infinitenature.org>).

The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model.

Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself.

The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db.

The project is intended to support the use of sequins(synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard library for quantitative analysis, modelling, and visualization of spike-in controls.

VoltRon is a novel spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is capable of analyzing multiple types and modalities of spatially-aware datasets. VoltRon visualizes and analyzes regions of interests (ROIs), spots, cells and even molecules.

This package contains three main functions including stddiff.numeric(), stddiff.binary() and stddiff.category(). These are used to calculate the standardized difference between two groups. It is especially used to evaluate the balance between two groups before and after propensity score matching.

This package provides algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent acceleration scheme. A tutorial style introduction to this package is available in a vignette.

This package provides a set of simple functions that transforms longitudinal data to estimate the cosinor linear model as described in Tong (1976). Methods are given to summarize the mean, amplitude and acrophase, to predict the mean annual outcome value, and to test the coefficients.

This package provides functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical.

YARD is a documentation generation tool for the Ruby programming language. It enables the user to generate consistent, usable documentation that can be exported to a number of formats very easily, and also supports extending for custom Ruby constructs such as custom class level definitions.

YARD is a documentation generation tool for the Ruby programming language. It enables the user to generate consistent, usable documentation that can be exported to a number of formats very easily, and also supports extending for custom Ruby constructs such as custom class level definitions.

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses). The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers decoupled and/or censored count data with two main functions. The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table. The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities.

This package is a computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The RadioSet class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating AUC or SF are included.

Perform one-dimensional spline regression with automatic knot selection. This package uses a penalized approach to select the most relevant knots. B-splines of any degree can be fitted. More details in Goepp et al. (2018)', "Spline Regression with Automatic Knot Selection", <arXiv:1808.01770>.

Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.

This package contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Doses (BRAID) model of combined action, along with tools for implementing other combination analysis methods, including Bliss independence, combination index, and additional response surface methods.

Clustered covariate regression enables estimation and inference in both linear and non-linear models with linear predictor functions even when the design matrix is column rank deficient. Routines in this package implement algorithms in Soale and Tsyawo (2019) <doi:10.13140/RG.2.2.32355.81441>.

Evaluation of the Carlson elliptic integrals and the incomplete elliptic integrals with complex arguments. The implementations use Carlson's algorithms <doi:10.1007/BF02198293>. Applications of elliptic integrals include probability distributions, geometry, physics, mechanics, electrodynamics, statistical mechanics, astronomy, geodesy, geodesics on conics, and magnetic field calculations.

This package provides functionality to infer trajectories from single-cell data, represent them into a common format, and adapt them. Other biological information can also be added, such as cellular grouping, RNA velocity and annotation. Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.

DAGs With Omitted Objects Displayed (DAGWOOD) is a framework to help reveal key hidden assumptions in a causal DAG. This package provides an implementation of the DAGWOOD algorithm. Further description can be found in Haber et al (2022) <DOI:10.1016/j.annepidem.2022.01.001>.

S4 classes for univariate and multivariate functional data with utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description of the package functionalities and its interplay with the MFPCA package for multivariate functional principal component analysis <https://CRAN.R-project.org/package=MFPCA>.

The goal of gnonadd is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.