Tensor-train is a compact representation for higher-order tensors. Some algorithms for performing tensor-train decomposition are available such as TT-SVD, TT-WOPT, and TT-Cross. For the details of the algorithms, see I. V. Oseledets (2011) <doi:10.1137/090752286>, Yuan Longao, et al (2017) <doi:10.48550/arXiv.1709.02641>, I. V. Oseledets (2010) <doi:10.1016/j.laa.2009.07.024>.

This package provides tools for converting data from complex or irregular layouts to a columnar structure. For example, tables with multilevel column or row headers, or spreadsheets. Header and data cells are selected by their contents and position, as well as formatting and comments where available, and are associated with one other by their proximity in given directions. Functions for data frames and HTML tables are provided.

This package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. It computes allele counts at individual variants (SNPs/SNVs), implements extensive QC (quality control) steps to remove problematic variants, and utilizes a Bayesian framework to identify statistically significant allele-specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples.

The first day of any MMWR week is Sunday. MMWR week numbering is sequential beginning with 1 and incrementing with each week to a maximum of 52 or 53. MMWR week #1 of an MMWR year is the first week of the year that has at least four days in the calendar year. This package provides functionality to convert dates to MMWR day, week, and year and the reverse.

Phylogenetic clustering (phyloclustering) is an evolutionary continuous time Markov Chain model-based approach to identify population structure from molecular data without assuming linkage equilibrium. The package phyclust provides a convenient implementation of phyloclustering for DNA and SNP data, capable of clustering individuals into subpopulations and identifying molecular sequences representative of those subpopulations. It is designed in C for performance and interfaced with R for visualization.

squallms is a Bioconductor R package that implements a "semi-labeled" approach to untargeted mass spectrometry data. It pulls in raw data from mass-spec files to calculate several metrics that are then used to label MS features in bulk as high or low quality. These metrics of peak quality are then passed to a simple logistic model that produces a fully-labeled dataset suitable for downstream analysis.

An R wrapper for agena.ai <https://www.agena.ai> which provides users capabilities to work with agena.ai using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to agena.ai cloud or local API for calculations. Note: running calculations requires a valid agena.ai API license (past the initial trial period of the local API).

It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a shiny app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).<DOI:10.1007/s42452-020-3120-6>.

The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) can be used to estimate the causal effect of a continuous exposure on a continuous outcome. This package provides an approximate sensitivity analysis of BAC with regards to the hyperparameter omega. BACprior also provides functions to guide the user in their choice of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).

Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).

Calculates and visualises cumulative percent decay curves, which are typically calculated from metagenomic taxonomic profiles. These can be used to estimate the level of expected endogenous taxa at different abundance levels retrieved from metagenomic samples, when comparing to samples of known sampling site or source. Method described in Fellows Yates, J. A. et. al. (2021) Proceedings of the National Academy of Sciences USA <doi:10.1073/pnas.2021655118>.

Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.

One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.

This package implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test.

This package provides a multi-layered untargeted pipeline for high-throughput LC/HRMS data processing to extract signals of organic small molecules. The package performs ion pairing, peak detection, peak table alignment, retention time correction, aligned peak table gap filling, peak annotation and visualization of extracted ion chromatograms (EICs) and total ion chromatograms (TICs). The IDSL.IPA package was introduced in <doi:10.1021/acs.jproteome.2c00120> .

This package provides methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel).

Nonparametric kernel distribution function estimation is performed. Three bandwidth selectors are implemented: the plug-in selectors of Altman and Leger and of Polansky and Baker, and the cross-validation selector of Bowman, Hall and Prvan. The exceedance function, the mean return period and the return level are also computed. For details, see Quintela-del-Rà o and Estévez-Pérez (2012) <doi:10.18637/jss.v050.i08>.

This package provides a set of all-cause and cause-specific life expectancy sensitivity and decomposition methods, including Arriaga (1984) <doi:10.2307/2061029>, others documented by Ponnapalli (2005) <doi:10.4054/DemRes.2005.12.7>, lifetable, numerical, and other algorithmic approaches such as Horiuchi et al (2008) <doi:10.1353/dem.0.0033>, or Andreev et al (2002) <doi:10.4054/DemRes.2002.7.14>.

This package provides functions and examples based on the m-out-of-n bootstrap suggested by Politis, D.N. and Romano, J.P. (1994) <doi:10.1214/aos/1176325770>. Additionally there are functions to estimate the scaling factor tau and the subsampling size m. For a detailed description and a full list of references, see Dalitz, C. and Lögler, F. (2025) <doi:10.32614/RJ-2025-031>.

This package performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).

Function pip3d() tests whether a point in 3D space is within, exactly on, or outside an enclosed surface defined by a triangular mesh. Function pip2d() tests whether a point in 2D space is within, exactly on, or outside a polygon. For a reference, see: Liu et al., A new point containment test algorithm based on preprocessing and determining triangles, Computer-Aided Design 42(12):1143-1150.

This package provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.

This package performs parametric synthesis of sounds with harmonic and noise components such as animal vocalizations or human voice. Also offers tools for audio manipulation and acoustic analysis, including pitch tracking, spectral analysis, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for synthesizing and annotating audio, manually correcting pitch contours, and measuring formant frequencies. Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.