This function performs genomic prediction of cross performance using genotype and phenotype data. It processes data in several steps including loading necessary software, converting genotype data, processing phenotype data, fitting mixed models, and predicting cross performance based on weighted marker effects. For more information, see Labroo et al. (2023) <doi:10.1007/s00122-023-04377-z>.

This package provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The implementation is in C++ and uses RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included.

Fits Generalised Linear Models (GLMs) with sparse and dense Matrix matrices for memory efficiency. Acts as a wrapper for the glm4() function in the MatrixModels package <doi:10.32614/CRAN.package.MatrixModels>, but adds convenient model methods and functions designed to mimic those associated with the glm() function from the stats package.

Detection of haplotype patterns that include single nucleotide polymorphisms (SNPs) and non-contiguous haplotypes that are associated with a phenotype. Methods for implementing HTRX are described in Yang Y, Lawson DJ (2023) <doi:10.1093/bioadv/vbad038> and Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>.

This package provides a Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.

Estimate test-retest reliability for complex sampling strategies and extract variances using IntraClass Effect Decomposition. Developed by Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" <doi:10.7554/eLife.35718> Also includes functions to simulate data based on sampling strategy. Unofficial version release name: "Good work squirrels".

Algorithms for multivariate outlier detection when missing values occur. Algorithms are based on Mahalanobis distance or data depth. Imputation is based on the multivariate normal model or uses nearest neighbour donors. The algorithms take sample designs, in particular weighting, into account. The methods are described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.

Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years.

This package provides a method of clustering functional data using subregion information of the curves. It is intended to supplement the fda and fda.usc packages in functional data object clustering. It also facilitates the printing and plotting of the results in a tree format and limits the partitioning candidates into a specific set of subregions.

Historic Pell grant data as provided by the US Department of Education. This package contains data about how much pell grant was awarded by which institution in which year. This data comes from the US Department of Education. Raw data can be downloaded from here: <https://www2.ed.gov/finaid/prof/resources/data/pell-institution.html>.

Computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing. For details, see Chen and Samworth (2016) <doi:10.1111/rssb.12137>.

The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added.

Create correlation networks using St. Nicolas House Analysis ('SNHA'). The package can be used for visualizing multivariate data similar to Principal Component Analysis or Multidimensional Scaling using a ranking approach. In contrast to MDS and PCA', SNHA uses a network approach to explore interacting variables. For details see Hermanussen et. al. 2021', <doi:10.3390/ijerph18041741>.

This package provides tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony. See, for instance, Sheppard et al (2016) <doi: 10.1038/NCLIMATE2991>, Sheppard et al (2017) <doi: 10.1051/epjnbp/2017000>, Sheppard et al (2019) <doi: 10.1371/journal.pcbi.1006744>.

Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" <doi:10.1007/BF00116466>, Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" <doi:10.1093/biostatistics/4.1.11>, Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" <doi:10.1214/17-AOAS1068>, and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" <doi:10.1016/j.sste.2021.100420>.

Compute price indices using various Hedonic and multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic Multilateral Time series re-estimation with splicing). The central function calculate_hedonic_index() offers a unified interface for running these methods on structured datasets. This package is designed to support index construction workflows across a wide range of domains â including but not limited to real estate â where quality-adjusted price comparisons over time are essential. The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands. The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024) <doi:10.1177/0282423X241246617>. For broader methodological context, see Eurostat (2013, ISBN:978-92-79-25984-5, <doi:10.2785/34007>).

This package allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model. Flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. Afpt can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions.

rmlint finds space waste and other broken things on your file system and offers to remove it. rmlint can find:

• duplicate files and duplicate directories,

• non-stripped binaries (i.e. binaries with debug symbols),

• broken symbolic links,

• empty files and directories,

• files with broken user and/or group ID.

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12.

In order to make Arrow Database Connectivity ('ADBC <https://arrow.apache.org/adbc/>) accessible from R, an interface compliant with the DBI package is provided, using driver back-ends that are implemented in the adbcdrivermanager framework. This enables interacting with database systems using the Arrow data format, thereby offering an efficient alternative to ODBC for analytical applications.

This package provides a method to filter correlation and covariance matrices by averaging bootstrapped filtered hierarchical clustering and boosting. See Ch. Bongiorno and D. Challet, Covariance matrix filtering with bootstrapped hierarchies (2020) <arXiv:2003.05807> and Ch. Bongiorno and D. Challet, Reactive Global Minimum Variance Portfolios with k-BAHC covariance cleaning (2020) <arXiv:2005.08703>.

The main function generateDataset() processes a user-supplied .R file that contains metadata parameters in order to generate actual data. The metadata parameters have to be structured in the form of metadata objects, the format of which is outlined in the package vignette. This approach allows to generate artificial data in a transparent and reproducible manner.

Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample. Package implementation is described in Domingo-Almenara et al. (2016) <doi:10.1021/acs.analchem.6b02927>.