Real-time quantitative polymerase chain reaction (qPCR) data sets by Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>. This package provides a dilution series for one PCR target: a random sequence that minimizes secondary structure and off-target primer binding. The data set is a six-point, ten-fold dilution series. For each concentration there are two replicates. Each amplification curve is 40 cycles long. Original raw data file: <https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.

Provide functions for overlaps clustering, fuzzy clustering and interval-valued data manipulation. The package implement the following algorithms: OKM (Overlapping Kmeans) from Cleuziou, G. (2007) <doi:10.1109/icpr.2008.4761079> ; NEOKM (Non-exhaustive overlapping Kmeans) from Whang, J. J., Dhillon, I. S., and Gleich, D. F. (2015) <doi:10.1137/1.9781611974010.105> ; Fuzzy Cmeans from Bezdek, J. C. (1981) <doi:10.1007/978-1-4757-0450-1> ; Fuzzy I-Cmeans from de A.T. De Carvalho, F. (2005) <doi:10.1016/j.patrec.2006.08.014>.

This package provides a general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://docs.ropensci.org/drake/> and the online manual <https://books.ropensci.org/drake/>.

The fftab package stores Fourier coefficients in a tibble and allows their manipulation in various ways. Functions are available for converting between complex, rectangular ('re', im'), and polar ('mod', arg') representations, as well as for extracting components as vectors or matrices. Inputs can include vectors, time series, and arrays of arbitrary dimensions, which are restored to their original form when inverting the transform. Since fftab stores Fourier frequencies as columns in the tibble, many standard operations on spectral data can be easily performed using tidy packages like dplyr'.

We implement various tests for the composite hypothesis of testing the fit to the family of inverse Gaussian distributions. Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. (2022) <doi:10.48550/arXiv.1910.14119>, as well as two tests from Henze and Klar (2002) <doi:10.1023/A:1022442506681>. Additionally, the package implements a test proposed by Baringhaus and Gaigall (2015) <doi:10.1016/j.jmva.2015.05.013>. For each test a parametric bootstrap procedure is implemented.

This package provides functions to compute a continuum of information-based measures for quantifying the temporal stability of populations, communities, and ecosystems, as well as their associated synchrony, based on species (or species assemblage) biomass, or other key variables. When biodiversity data are available, the package also enables the assessment of the corresponding diversityâ stability and diversityâ synchrony relationships. All measures are applicable in both temporal and spatial contexts. The theoretical and methodological background is detailed in Chao et al. (2025) <doi:10.1101/2025.08.20.671203>.

Lightweight utilities for nucleic acid melting curve analysis are important in life sciences and diagnostics. This software can be used for the analysis and presentation of melting curve data from microbead-based assays (surface melting curve analysis) and reactions in solution (e.g., quantitative PCR (qPCR), real-time isothermal Amplification). Further information are described in detail in two publications in The R Journal [ <https://journal.r-project.org/archive/2013-2/roediger-bohm-schimke.pdf>; <https://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf>].

This package provides a data generator of multivariate non-normal data in R. It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 <doi:10.3758/s13428-019-01291-5>), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis.

We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.

This package performs smoothed (and non-smoothed) principal/independent components analysis of functional data. Various functional pre-whitening approaches are implemented as discussed in Vidal and Aguilera (2022) â Novel whitening approaches in functional settings", <doi:10.1002/sta4.516>. Further whitening representations of functional data can be derived in terms of a few principal components, providing an avenue to explore hidden structures in low dimensional settings: see Vidal, Rosso and Aguilera (2021) â Bi-smoothed functional independent component analysis for EEG artifact removalâ , <doi:10.3390/math9111243>.

This package provides tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores.

Data sets and functions to support the books "Statistics: Data analysis and modelling" by Speekenbrink, M. (2021) <https://mspeekenbrink.github.io/sdam-book/> and "An R companion to Statistics: data analysis and modelling" by Speekenbrink, M. (2021) <https://mspeekenbrink.github.io/sdam-r-companion/>. All datasets analysed in these books are provided in this package. In addition, the package provides functions to compute sample statistics (variance, standard deviation, mode), create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus tests and tests of individual contrasts.

This package provides a toolbox for meta-analysis. This package includes: 1,a robust multivariate meta-analysis of continuous or binary outcomes; 2, a bivariate Egger's test for detecting small study effects; 3, Galaxy Plot: A New Visualization Tool of Bivariate Meta-Analysis Studies; 4, a bivariate T&F method accounting for publication bias in bivariate meta-analysis, based on symmetry of the galaxy plot. Hong C. et al(2020) <doi:10.1093/aje/kwz286>, Chongliang L. et al(2020) <doi:10.1101/2020.07.27.20161562>.

Reads comma-separated and other delimited text files into base R data frames using the zsv C parser <https://github.com/liquidaty/zsv> as its native backend. Handles quoted fields, embedded delimiters and newlines, escaped quotes, and both LF and CRLF line endings, and infers logical, integer, double, and character column types. Column types can also be given explicitly, in which case a value that cannot be converted is an error rather than a silently missing value. The zsv sources are bundled, so there are no runtime dependencies.

The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering.

DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data.

Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management. Introduction to HCRs provides a simple overview to how HCRs work. Users are able to select their own HCR and step through its performance, year by year. Biological variability and estimation uncertainty are introduced. Measuring performance builds on the previous app and introduces the idea of using performance indicators to measure HCR performance. Comparing performance allows multiple HCRs to be created and tested, and their performance compared so that the preferred HCR can be selected.

Discovers Global Light Commons data packages through their registry, opens immutable passing revisions, and provides searchable inventories of package metadata. Selected metadata and measurement files can be downloaded or imported with metadata-defined columns, types, factor levels, date-time values, and time zones. Git Large File Storage objects are resolved without requiring an external Git LFS installation, and imported file groups can be explicitly collected into data suitable for personal light exposure analysis workflows. An included shiny application supports interactive discovery, inspection, selection, preview, and reproducible handoff to R'.

Calculate dissolved gas concentrations from raw MIMS (Membrane Inlet Mass Spectrometer) signal data. Use mimsy() on a formatted CSV file to return dissolved gas concentrations (mg and microMole) of N2, O2, Ar based on gas solubility at temperature, pressure, and salinity. See references Benson and Krause (1984), Garcia and Gordon (1992), Stull (1947), and Hamme and Emerson (2004) for more information. Easily save the output to a nicely-formatted multi-tab Excel workbook with mimsy.save(). Supports dual-temperature standard calibration for dual-bath MIMS setups.

Three generalizations of the synthetic control method (which has already an implementation in package Synth') are implemented: first, MSCMT allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klöà ner (2018) <doi:10.1016/j.ecosta.2017.08.002>.

Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. The naive analysis which is solely based on the observed data without adjustment may lead to bias. The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given validation set. This package is an implementation of Chen (2013) <doi:10.1002/bimj.201200195>.

This package provides functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186. Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.

Perform an exploration and a preliminary analysis on the dose- response relationship of nanomaterial toxicity. Several functions are provided for data exploration, including functions for creating a subset of dataset, frequency tables and plots. Inference for order restricted dose- response data is performed by testing the significance of monotonic dose-response relationship, using Williams, Marcus, M, Modified M and Likelihood ratio tests. Several methods of multiplicity adjustment are also provided. Description of the methods can be found in <https://github.com/rahmasarina/dose-response-analysis/blob/main/Methodology.pdf>.

Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) <doi:10.1002/gepi.22115>. It first conducts a meta-filtering test to filter out unpromising SNPs by combining all samples in the consortia data. It then runs a test of omnibus-filtering-based GxE meta-analysis (ofGEM) that combines the strengths of the fixed- and random-effects meta-analysis with meta-filtering. It can also analyze data from multiple ethnic groups.