This package calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (including median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package boot. Both one- and two-sided intervals are supported.

syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-based phylogeny reconstruction with maximum likelihood.

Evaluate, fit, and analyze Hill dose response models (Goutelle et al., 2008 <doi:10.1111/j.1472-8206.2008.00633.x>), also sometimes referred to as four-parameter log-logistic models. Includes tools to invert Hill models, select models based on the Akaike information criterion (Akaike, 1974 <doi:10.1109/TAC.1974.1100705>) or Bayesian information criterion (Schwarz, 1978 <https://www.jstor.org/stable/2958889>), and construct bootstrapped confidence intervals both on the Hill model parameters and values derived from the Hill model parameters.

The level-dependent cross-validation method is implemented for the selection of thresholding value in wavelet shrinkage. This procedure is implemented by coupling a conventional cross validation with an imputation method due to a limitation of data length, a power of 2. It can be easily applied to classical leave-one-out and k-fold cross validation. Since the procedure is computationally fast, a level-dependent cross validation can be performed for wavelet shrinkage of various data such as a data with correlated errors.

This package provides tools for storing and managing competition results. Competition is understood as a set of games in which players gain some abstract scores. There are two ways for storing results: in long (one row per game-player) and wide (one row per game with fixed amount of players) formats. This package provides functions for creation and conversion between them. Also there are functions for computing their summary and Head-to-Head values for players. They leverage grammar of data manipulation from dplyr'.

Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) <doi:10.48550/arXiv.1707.01815>, Gaure (2013) <doi:10.1016/j.csda.2013.03.024>, Berge (2018) <https://ideas.repec.org/p/luc/wpaper/18-13.html>, and Correia et al. (2020) <doi: 10.1177/1536867X20909691>. This implementation is described in Vargas Sepulveda (2025) <doi:10.1371/journal.pone.0331178>.

This package provides a modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.

Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) <doi:10.1002/sim.2287>.

This package provides a model for leaf fluorescence, reflectance and transmittance spectra. It implements the model introduced by Vilfan et al. (2016) <DOI:10.1016/j.rse.2016.09.017>. Fluspect-B calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II.

This package provides tools for training and analysing fairness-aware gated neural networks for subgroup-aware prediction and interpretation in clinical datasets. Methods draw on prior work in mixture-of-experts neural networks by Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>, fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>, and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) <doi:10.1016/j.jpsychires.2016.03.016>.

Supports teaching methods of estimating and testing time series factor models for use in robust portfolio construction and analysis. Unique in providing not only classical least squares, but also modern robust model fitting methods which are not much influenced by outliers. Includes returns and risk decompositions, with user choice of standard deviation, value-at-risk, and expected shortfall risk measures. "Robust Statistics Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, M. Salibian-Barrera (2019) <doi:10.1002/9781119214656>.

Builds a LASSO, Ridge, or Elastic Net model with glmnet or cv.glmnet with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process. The methods are described in Friedman et al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al. (2011) <doi:10.18637/jss.v039.i05>.

This package provides two functions that implement the one-sided and two-sided versions of the Hodrick-Prescott filter. The one-sided version is a Kalman filter-based implementation, whereas the two- sided version uses sparse matrices for improved efficiency. References: Hodrick, R. J., and Prescott, E. C. (1997) <doi:10.2307/2953682> Mcelroy, T. (2008) <doi:10.1111/j.1368-423X.2008.00230.x> Meyer-Gohde, A. (2010) <https://ideas.repec.org/c/dge/qmrbcd/181.html> For more references, see the vignette.

This package contains procedures to estimate the nine condensed Jacquard genetic identity coefficients (Jacquard, 1974) <doi:10.1007/978-3-642-88415-3> by constrained least squares (Graffelman et al., 2024) <doi:10.1101/2024.03.25.586682> and by the method of moments (Csuros, 2014) <doi:10.1016/j.tpb.2013.11.001>. These procedures require previous estimation of the allele frequencies. Functions are supplied that estimate relationship parameters that derive from the Jacquard coefficients, such as individual inbreeding coefficients and kinship coefficients.

This package provides tools for privacy-preserving meta-analysis of function-valued quantities across heterogeneous studies. Implements the MetaHunt pipeline, including the denoised functional Successive Projection Algorithm (d-fSPA) for basis hunting, constrained weight estimation, Dirichlet regression of weights on study-level covariates, target prediction, and split/cross conformal prediction intervals. Operates on aggregate-level function evaluations, so individual-level data from source studies are not required. Methodology described in Shi, Imai, and Zhang (2026) <doi:10.48550/arXiv.2604.23847>.

Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach.

This package provides a generalised data structure for fast and efficient loading and data munching of sparse omics data. The OmicFlow requires an up-front validated metadata template from the user, which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an omics class. Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated autoFlow method to generate a comprehensive report.

Portable /proc/self/maps as a data frame. Determine which library or other region is mapped to a specific address of a process. -- R packages can contain native code, compiled to shared libraries at build or installation time. When loaded, each shared library occupies a portion of the address space of the main process. When only a machine instruction pointer is available (e.g. from a backtrace during error inspection or profiling), the address space map determines which library this instruction pointer corresponds to.

Computes robust p-values for overall fit and nested comparisons of structural equation models fitted with lavaan'. Implements penalized eigenvalue block averaging and penalized regression (Foldnes, Moss, Grønneberg, 2025) <doi:10.1080/10705511.2024.2372028>, including their extension to nested models (Foldnes, Grønneberg, Moss, 2026) <doi:10.3758/s13428-026-02968-4>, alongside familiar corrections such as Satorra-Bentler. Supported settings include complete-data ML, GLS, and ULS, categorical DWLS and ULS, and full-information maximum likelihood with one or several groups.

This package provides a set of RStudio addins that are designed to be used in combination with user-defined RStudio keyboard shortcuts. These addins either: 1) insert text at a cursor position (e.g. insert operators %>%, <<-, %$%, etc.), 2) replace symbols in selected pieces of text (e.g., convert backslashes to forward slashes which results in stings like "c:\data\" converted into "c:/data/") or 3) enclose text with special symbols (e.g., converts "bold" into "**bold**") which is convenient for editing R Markdown files.

Generalized estimating equations (GEE) are a popular choice for analyzing longitudinal binary outcomes. This package provides an interface for fitting GEE, currently for logistic regression, within the tern <https://cran.r-project.org/package=tern> framework (Zhu, Sabanés Bové et al., 2023) and tabulate results easily using rtables <https://cran.r-project.org/package=rtables> (Becker, Waddell et al., 2023). It builds on geepack <doi:10.18637/jss.v015.i02> (Højsgaard, Halekoh and Yan, 2006) for the actual GEE model fitting.

This package implements wild bootstrap tests for autocorrelation in Vector Autoregressive (VAR) models based on Ahlgren and Catani (2016) <doi:10.1007/s00362-016-0744-0>, a combined Lagrange Multiplier (LM) test for Autoregressive Conditional Heteroskedasticity (ARCH) in VAR models from Catani and Ahlgren (2016) <doi:10.1016/j.ecosta.2016.10.006>, and bootstrap-based methods for determining the cointegration rank from Cavaliere, Rahbek, and Taylor (2012) <doi:10.3982/ECTA9099> and Cavaliere, Rahbek, and Taylor (2014) <doi:10.1080/07474938.2013.825175>.

This package provides a comprehensive data analysis framework for NIH-funded research that streamlines workflows for both data cleaning and preparing NIH Data Archive ('NDA') submission templates. Provides unified access to multiple data sources ('REDCap', MongoDB', Qualtrics', SQL', ORACLE') through interfaces to their APIs, with specialized functions for data cleaning, filtering, merging, and parsing. Features automatic validation, field harmonization, and memory-aware processing to enhance reproducibility in multi-site collaborative research as described in Mittal et al. (2021) <doi:10.20900/jpbs.20210011>.

This package implements nested cross-validation applied to the glmnet and caret packages. With glmnet this includes cross-validation of elastic net alpha parameter. A number of feature selection filter functions (t-test, Wilcoxon test, ANOVA, Pearson/Spearman correlation, random forest, ReliefF) for feature selection are provided and can be embedded within the outer loop of the nested CV. Nested CV can be also be performed with the caret package giving access to the large number of prediction methods available in caret.