GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait models. This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package.

This package provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.

The PSMatch package helps proteomics practitioners to load, handle and manage peptide spectrum matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions.

This package offers features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the autoplot() generic of ggplot2. The mlr3viz package draws plots with the viridis color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and precision-recall curves.

This package provides tools to identify global ("unknown" or "free") objects in R expressions by code inspection using various strategies, e.g. conservative or liberal. The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in distributed compute environments.

Puma is a simple, fast, threaded, and highly concurrent HTTP 1.1 server for Ruby/Rack applications. Puma is intended for use in both development and production environments. In order to get the best throughput, it is highly recommended that you use a Ruby implementation with real threads like Rubinius or JRuby.

This package provides a function to format R source code. Spaces and indent will be added to the code automatically, and comments will be preserved under certain conditions, so that R code will be more human-readable and tidy. There is also a Shiny app as a user interface in this package.

Simulates individual-based models of agricultural pest management and the evolution of pesticide resistance. Management occurs on a spatially explicit landscape that is divided into an arbitrary number of farms that can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest genomes are modelled in a way that allows for any number of pest traits with an arbitrary covariance structure that is constructed using an evolutionary algorithm in the mine_gmatrix() function. Simulations are then run using the run_farm_sim() function. This package thereby allows for highly mechanistic social-ecological models of the evolution of pesticide resistance under different types of crop rotation and pesticide application regimes.

This package provides functions to fit Gaussian linear model by maximising the residual log likelihood where the covariance structure can be written as a linear combination of known matrices. Can be used for multivariate models and random effects models. Easy straight forward manner to specify random effects models, including random interactions. Code now optimised to use Sherman Morrison Woodbury identities for matrix inversion in random effects models. We've added the ability to fit models using any kernel as well as a function to return the mean and covariance of random effects conditional on the data (best linear unbiased predictors, BLUPs). Clifford and McCullagh (2006) <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.

Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. rumidas also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.

Predicts antimicrobial peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The AmpGram model is too large for CRAN and it has to be downloaded separately from the repository: <https://github.com/michbur/AmpGramModel>.

Best subset glm using information criteria or cross-validation, carried by using leaps algorithm (Furnival and Wilson, 1974) <doi:10.2307/1267601> or complete enumeration (Morgan and Tatar, 1972) <doi:10.1080/00401706.1972.10488918>. Implements PCR and PLS using AIC/BIC. Implements one-standard deviation rule for use with the caret package.

Provide standard tables, listings, and graphs (TLGs) libraries used in clinical trials. This package implements a structure to reformat the data with dunlin', create reporting tables using rtables and tern with standardized input arguments to enable quick generation of standard outputs. In addition, it also provides comprehensive data checks and script generation functionality.

Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.

Perform additional multiple testing procedure methods to p.adjust(), such as weighted Hochberg (Tamhane, A. C., & Liu, L., 2008) <doi:10.1093/biomet/asn018>, ICC adjusted Bonferroni method (Shi, Q., Pavey, E. S., & Carter, R. E., 2012) <doi:10.1002/pst.1514> and a new correlation corrected weighted Hochberg for correlated endpoints.

This package provides a concise check of the format of one or multiple input arguments (data type, length or value) is provided. Since multiple input arguments can be tested simultaneously, a lengthly list of checks at the beginning of your function can be avoided, hereby enhancing the readability and maintainability of your code.

Biotracers and stomach content analyses are combined in a Bayesian hierarchical model to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix (all diet proportions). The package relies on the JAGS software and the jagsUI package to run a Markov chain Monte Carlo approximation of the different variables.

The epilogi variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics, 39(9): btad545. <doi:10.1093/bioinformatics/btad545>.

This package provides a toolkit for calculating forest and canopy structural complexity metrics from terrestrial LiDAR (light detection and ranging). References: Atkins et al. 2018 <doi:10.1111/2041-210X.13061>; Hardiman et al. 2013 <doi:10.3390/f4030537>; Parker et al. 2004 <doi:10.1111/j.0021-8901.2004.00925.x>.

Extra geoms and scales for ggplot2', including geom_cloud(), a Normal density cloud replacement for errorbars; transforms ssqrt_trans and pseudolog10_trans, which are loglike but appropriate for negative data; interp_trans() and warp_trans() which provide scale transforms based on interpolation; and an infix compose operator for scale transforms.

Offers a convenient way to compute parameters in the framework of the theory of vocational choice introduced by J.L. Holland, (1997). A comprehensive summary to this theory of vocational choice is given in Holland, J.L. (1997). Making vocational choices. A theory of vocational personalities and work environments. Lutz, FL: Psychological Assessment.

Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.

Linear splines with convenient parametrisations such that (1) coefficients are slopes of consecutive segments or (2) coefficients are slope changes at consecutive knots. Knots can be set manually or at break points of equal-frequency or equal-width intervals covering the range of x'. The implementation follows Greene (2003), chapter 7.2.5.

Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.