This package provides methods to simulate and analyse the size and length of branching processes with an arbitrary offspring distribution. These can be used, for example, to analyse the distribution of chain sizes or length of infectious disease outbreaks, as discussed in Farrington et al. (2003) <doi:10.1093/biostatistics/4.2.279>.

Construction, calculation and display of fault trees. Methods derived from Clifton A. Ericson II (2005, ISBN: 9780471739425) <DOI:10.1002/0471739421>, Antoine Rauzy (1993) <DOI:10.1016/0951-8320(93)90060-C>, Tim Bedford and Roger Cooke (2012, ISBN: 9780511813597) <DOI:10.1017/CBO9780511813597>, Nikolaos Limnios, (2007, ISBN: 9780470612484) <DOI: 10.1002/9780470612484>.

The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design. The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.

Additional annotations, stats, geoms and scales for plotting "light" spectra with ggplot2', together with specializations of ggplot() and autoplot() methods for spectral data and waveband definitions stored in objects of classes defined in package photobiology'. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

This package provides tools for analyzing Marshall-Olkin shock models semi-independent time. It includes interactive shiny applications for exploring copula-based dependence structures, along with functions for modeling and visualization. The methods are based on Mijanovic and Popovic (2024, submitted) "An R package for Marshall-Olkin shock models with semi-independent times.".

Two novel matching-based methods for estimating group average treatment effects (GATEs). The match_y1y0() and match_y1y0_bc() functions are used for imputing the potential outcomes based on matching and bias-corrected matching techniques, respectively. The EstGATE() function is employed to estimate the GATE after imputing the potential outcomes.

It implements the online Bayesian methods for change point analysis. It can also perform missing data imputation with methods from VIM'. The reference is Yigiter A, Chen J, An L, Danacioglu N (2015) <doi:10.1080/02664763.2014.1001330>. The link to the package is <https://CRAN.R-project.org/package=onlineBcp>.

Run simulations or other functions while easily varying parameters from one iteration to the next. Some common use cases would be grid search for machine learning algorithms, running sets of simulations (e.g., estimating statistical power for complex models), or bootstrapping under various conditions. See the paramtest documentation for more information and examples.

Nomograms are constructed to predict the cumulative incidence rate which is calculated after adjusting for competing causes to the event of interest. K-fold cross-validation is implemented to validate predictive accuracy using a competing-risk version of the concordance index. Methods are as described in: Kattan MW, Heller G, Brennan MF (2003).

This package creates some tables of clinical study. Table 1 is created by table1() to describe baseline characteristics, which is essential in every clinical study. Created by table2(), the function of Table 2 is to explore influence factors. And Table 3 created by table3() is able to make stratified analysis.

The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered.

The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT.

transmogR provides the tools needed to crate a new reference genome or reference transcriptome, using a set of variants. Variants can be any combination of SNPs, Insertions and Deletions. The intended use-case is to enable creation of variant-modified reference transcriptomes for incorporation into transcriptomic pseudo-alignment workflows, such as salmon.

The tidyomics ecosystem is a set of packages for ’omic data analysis that work together in harmony; they share common data representations and API design, consistent with the tidyverse ecosystem. The tidyomics package is designed to make it easy to install and load core packages from the tidyomics ecosystem with a single command.

This package provides a client for the OmniPath web service and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data.

This package provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples.

This package is a collection of data analysis tools. It includes tools for regression outlier detection in a fitted linear model, stationary bootstrap using a truncated geometric distribution, a comprehensive test for weak stationarity, column means by group, weighted biplots, and a heuristic to obtain a better initial configuration in non-metric MDS.

Relx assembles releases for an Erlang/OTP release. Given a release specification and a list of directories in which to search for OTP applications it will generate a release output. That output depends heavily on what plugins available and what options are defined, but usually it is simply a well configured release directory.

Contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e., the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997).

This package provides tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See <http://aire.cdmx.gob.mx/> for more information.

Set of functions to import COVID-19 pandemic data into R. The Brazilian COVID-19 data, obtained from the official Brazilian repository at <https://covid.saude.gov.br/>, is available at country, region, state, and city-levels. The package also downloads the world-level COVID-19 data from the John Hopkins University's repository.

Small toolbox for data analyses in environmental chemistry and ecotoxicology. Provides, for example, calibration() to calculate calibration curves and corresponding limits of detection (LODs) and limits of quantification (LOQs) according to German DIN 32645 (2008). texture() makes it easy to estimate soil particle size distributions from hydrometer measurements (ASTM D422-63, 2007).

Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and fmeffects computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <doi:10.48550/arXiv.2201.08837>.

This package provides a greta (Golding (2019) <doi:10.21105/joss.01601>) module that lets you use mgcv smoother functions and formula syntax to define smooth terms for use in a greta model. You can then define your own likelihood to complete the model, and fit it by Markov Chain Monte Carlo (MCMC).