Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user. Also fits the user specified distribution to a given data set. More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017) CompDist: Multisection composite distributions.

Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and McCaffrey <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ>. Functions used in Huang and Li <doi:10.3758/s13428-021-01627-0>, Huang, Wiedermann', and Zhang <doi:10.1080/00273171.2022.2077290>, and Huang, Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness).

The CoTiMA package performs meta-analyses of correlation matrices of repeatedly measured variables taken from studies that used different time intervals. Different time intervals between measurement occasions impose problems for meta-analyses because the effects (e.g. cross-lagged effects) cannot be simply aggregated, for example, by means of common fixed or random effects analysis. However, continuous time math, which is applied in CoTiMA', can be used to extrapolate or intrapolate the results from all studies to any desired time lag. By this, effects obtained in studies that used different time intervals can be meta-analyzed. CoTiMA fits models to empirical data using the structural equation model (SEM) package ctsem', the effects specified in a SEM are related to parameters that are not directly included in the model (i.e., continuous time parameters; together, they represent the continuous time structural equation model, CTSEM). Statistical model comparisons and significance tests are then performed on the continuous time parameter estimates. CoTiMA also allows analysis of publication bias (Egger's test, PET-PEESE estimates, zcurve analysis etc.) and analysis of statistical power (post hoc power, required sample sizes). See Dormann, C., Guthier, C., & Cortina, J. M. (2019) <doi:10.1177/1094428119847277>. and Guthier, C., Dormann, C., & Voelkle, M. C. (2020) <doi:10.1037/bul0000304>.

This package provides a system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) <doi:10.1101/2023.03.11.532179>.

This package implements the algorithm described in Trapnell,C. et al. (2010) <doi: 10.1038/nbt.1621>. This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using biomaRt package, and the mean fragment lengths which can be calculated using the CollectInsertSizeMetrics(Picard) tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset.

Helps visualizing what is summarized in Pearson's correlation coefficient. That is, it visualizes its main constituent, namely the distances of the single values to their respective mean. The visualization thereby shows what the etymology of the word correlation contains: In pairwise combination, bringing back (see package Vignette for more details). I hope that the correlatio package may benefit some people in understanding and critically evaluating what Pearson's correlation coefficient summarizes in a single number, i.e., to what degree and why Pearson's correlation coefficient may (or may not) be warranted as a measure of association.

Automatically builds 12 classification models from data. The package also returns 25 plots, 5 tables and a summary report.

This package provides a copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2019). "Clustering dependent observations with copula functions". Statistical Papers, 60, p.35-51. <doi:10.1007/s00362-016-0822-3>.

Deriving skill structures from skill assignment data for courses (sets of learning objects).

Computes p-values using the largest root test using an approximation to the null distribution by Johnstone (2008) <DOI:10.1214/08-AOS605>.

We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.

Generate project files and directories following a pre-made template. You can specify variables to customize file names and content, and flexibly adapt the template to your needs. cookiecutter for R implements a subset of the excellent cookiecutter package for the Python programming language (<https://github.com/cookiecutter/>), and aims to be largely compatible with the original cookiecutter template format.

Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.

This package provides functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network). Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.

Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. The package makes it easy to incorporate constraints into the model's coefficients. The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), a vector of bound constraints for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions among coefficients (i.e beta1 > beta2). The references of this packages are the same as stats package for glm() and arima() functions. See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528). For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages.

Defines the classes used for "class comparison" problems in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class comparison includes tests for differential expression; see Simon's book for details on typical problem types.

Record and generate a gif of your R sessions plots. When creating a visualization, there is inevitably iteration and refinement that occurs. Automatically save the plots made to a specified directory, previewing them as they would be saved. Then combine all plots generated into a gif to show the plot refinement over time.

This package provides a local haplotyping visualization toolbox to capture major patterns of co-inheritance between clusters of linked variants, whilst connecting findings to phenotypic and demographic traits across individuals. crosshap enables users to explore and understand genomic variation across a trait-associated region. For an example of successful local haplotype analysis, see Marsh et al. (2022) <doi:10.1007/s00122-022-04045-8>.

The analysis of conflicting claims arises when an amount has to be divided among a set of agents with claims that exceed what is available. A rule is a way of selecting a division among the claimants. This package computes the main rules introduced in the literature from ancient times to the present. The inventory of rules covers the proportional and the adjusted proportional rules, the constrained equal awards and the constrained equal losses rules, the constrained egalitarian, the Pinilesâ and the minimal overlap rules, the random arrival and the Talmud rules. Besides, the Dominguez and Thomson and the average-of-awards rules are also included. All of them can be found in the book by W. Thomson (2019), How to divide when there isn't enough. From Aristotle, the Talmud, and Maimonides to the axiomatics of resource allocation', except for the average-of-awards rule, introduced by Mirás Calvo et al. (2022), <doi:10.1007/s00355-022-01414-6>. In addition, graphical diagrams allow the user to represent, among others, the set of awards, the paths of awards, the schedules of awards of a rule, and some indexes. A good understanding of the similarities and differences between the rules is useful for better decision-making. Therefore, this package could be helpful to students, researchers, and managers alike. For a more detailed explanation of the package, see Mirás Calvo et al. (2023), <doi:10.1016/j.dajour.2022.100160>.

Enables DBI compliant packages to integrate with the RStudio connections pane, and the pins package. It automates the display of schemata, tables, views, as well as the preview of the table's top 1000 records.

Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) <doi:10.1101/2021.09.03.458920> and Bridgeford et al. (2023) <doi:10.48550/arXiv.2307.13868>. Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups.

Simulates parameterized single- and double-directional stem deformations in tree point clouds derived from terrestrial or mobile laser scanning, enabling the generation of realistic synthetic datasets for training and validating machine learning models in wood defect detection, quality assessment, and precision forestry. For more details see Pires (2025) <doi:10.54612/a.7hln0kr0ta>.

This package performs the calibration procedure proposed by Sung et al. (2018+) <arXiv:1806.01453>. This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest.

Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" <https://theeffectbook.net>, first and second edition, Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.