Dataset and functions to explore quality of literary novels. The package is a part of the Riddle of Literary Quality project, and it contains the data of a reader survey about fiction in Dutch, a description of the novels the readers rated, and the results of stylistic measurements of the novels. The package also contains functions to combine, analyze, and visualize these data. For more details, see: Eder M, van Zundert J, Lensink S, van Dalen-Oskam K (2022). Replicating The Riddle of Literary Quality: The litRiddle package for R. In _Digital Humanities 2022: Conference Abstracts_, 636-637.

Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and McCaffrey (2002) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ> is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X).

This package provides tools for visualizing and analyzing the shape of discrete nominal frequency distributions. The package introduces centered frequency plots, in which nominal categories are ordered from the most frequent category at the center toward less frequent categories on both sides, facilitating the detection of distributional patterns such as uniformity, dominance, symmetry, skewness, and long-tail behavior. In addition, the package supports Pareto charts for the study of dominance and cumulative frequency structure in nominal data. The package is designed for exploratory data analysis and statistical teaching, offering visualizations that emphasize distributional form rather than arbitrary category ordering.

An implementation of data analysis tools for samples of symmetric or Hermitian positive definite matrices, such as collections of covariance matrices or spectral density matrices. The tools in this package can be used to perform: (i) intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive definite matrices with applications to dimension reduction, denoising and clustering in the space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference for samples of positive definite matrices by means of intrinsic data depth functions and rank-based hypothesis tests in the space of Hermitian positive definite matrices.

This package provides functionalities for performing stability analysis of genotype by environment interaction (GEI) to identify superior and stable genotypes across diverse environments. It implements Eberhart and Russellâ s ANOVA method (1966)(<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinsonâ s Joint Linear Regression method (1963) (<doi:10.1071/AR9630742>), Wrickeâ s Ecovalence (1962, 1964), Shuklaâ s stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>), Kangâ s simultaneous selection for high yield and stability (1991) (<doi:10.2134/agronj1991.00021962008300010037x>), Additive Main Effects and Multiplicative Interaction (AMMI) method and Genotype plus Genotypes by Environment (GGE) Interaction methods.

The Subsemble algorithm is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. The paper, "Subsemble: An ensemble method for combining subset-specific algorithm fits" is authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014) <doi:10.1080/02664763.2013.864263>.

Fetch UK housing data from official sources. Access the UK House Price Index and Price Paid Data from HM Land Registry <https://landregistry.data.gov.uk/>, domestic and non-domestic Energy Performance Certificates from the MHCLG Open Data service <https://epc.opendatacommunities.org/>, and planning application, brownfield land, and local plan data from planning.data.gov.uk <https://www.planning.data.gov.uk/>. Data covers all 441 UK local authorities from 1995 to the present. Functions accept flexible filters (postcode, local authority, property type, date range) and return tidy data frames. Downloaded data is cached locally for subsequent calls.

This package provides penalized accelerated failure time (AFT) model estimation for right-censored and partly interval-censored survival data using induced smoothing and coordinate descent algorithms. Supported penalties include broken adaptive ridge (BAR), LASSO, adaptive LASSO, and SCAD. Core estimation routines are implemented in C++ via Rcpp and RcppArmadillo for computational efficiency. The methodology is related to Zeng and Lin (2008) <doi:10.1093/biostatistics/kxm034>, Xu et al. (2010) <doi:10.1002/sim.2576>, Dai et al. (2018) <doi:10.1016/j.jmva.2018.08.007>, and Choi et al. (2025) <doi:10.48550/arXiv.2503.11268>.

High performance principal component analysis routines that operate directly on bigmemory::big.matrix() objects. The package avoids materialising large matrices in memory by streaming data through BLAS and LAPACK kernels and provides helpers to derive scores, loadings, correlations, and contribution diagnostics, including utilities that stream results into bigmemory'-backed matrices for file-based workflows. Additional interfaces expose scalable singular value decomposition, robust PCA, and robust SVD algorithms so that users can explore large matrices while tempering the influence of outliers. Scalable principal component analysis is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) <doi:10.1145/2723372.2751520>.

This package implements the DANDELION method for prioritizing candidate disease-related proximal genes by integrating trans-regulatory association p-values and gene-level trait association p-values. The statistical testing framework builds on the divide-aggregate composite-null test described by Liu et al. (2022) <doi:10.1080/01621459.2021.1914634>. The biological application and SNP/gene-based prioritization workflow are motivated by Salamone et al. (under review), "Leveraging trans-gene regulation prioritizes central genes and pathways in asthma". The package provides functions for identifying distal-proximal gene pairs, organizing significant pairs into genomic loci, and visualizing resulting gene networks.

This package provides density functions for the joint distribution of choice, response time and confidence for discrete confidence judgments as well as functions for parameter fitting, prediction and simulation for various dynamical models of decision confidence. All models are explained in detail by Hellmann et al. (2023; Preprint available at <https://osf.io/9jfqr/>, published version: <doi:10.1037/rev0000411>). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, <doi:10.1037/a0019737>), and various race models. C++ code for dynWEV and 2DSD is based on the rtdists package by Henrik Singmann.

This package provides robustness checks to align estimands with the identification that they require. Given a dagitty object and a model specification, DAGassist classifies variables by causal roles, recovers a target estimand, and generates a report comparing the original model with DAG-derived adjustment sets. Exports publication-grade reports in LaTeX', Word', Excel', dotwhisker', or plain text/'markdown'. DAGassist is built on dagitty', an R package that uses the DAGitty web tool (<https://dagitty.net/>) for creating and analyzing DAGs. Methods draw on Pearl (2009) <doi:10.1017/CBO9780511803161> and Textor et al. (2016) <doi:10.1093/ije/dyw341>.

This package implements functional propensity score (FPS) weighting for causal inference with functional treatments. The method estimates weights that balance observed confounders by removing their dependence on the functional treatment and uses a dual formulation of the weighting problem for efficient unconstrained optimization. The framework supports scalar, binary, and functional outcomes, as well as functional covariates, and can be used to estimate marginal causal effects in settings with time-varying exposures. The methodology follows Ciardulli, S., Fontana, N., Vantini, S., and Ieva, F. (2026) "Generalized propensity score weighting for functional causal inference framework" <doi:10.48550/arXiv.2608.03200>.

Different functions includes constructing composite indicators, imputing missing data, and evaluating imputation techniques. Additionally, different tools for data normalization. Detailed methodologies of Indicator package are: OECD/European Union/EC-JRC (2008), "Handbook on Constructing Composite Indicators: Methodology and User Guide", OECD Publishing, Paris, <DOI:10.1787/533411815016>, Matteo Mazziotta & Adriano Pareto, (2018) "Measuring Well-Being Over Time: The Adjusted Mazziottaâ Pareto Index Versus Other Non-compensatory Indices" <DOI:10.1007/s11205-017-1577-5> and De Muro P., Mazziotta M., Pareto A. (2011), "Composite Indices of Development and Poverty: An Application to MDGs" <DOI:10.1007/s11205-010-9727-z>.

Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view <doi:10.1093/bioinformatics/btx682>. A method to select (as well as funtions to display) important variables is also provided <doi:10.1093/nargab/lqac014>.

This package provides transparent tools for fitting and evaluating analytical calibration curves. Ordinary and weighted least squares fits are supported, together with lack-of-fit, heteroscedasticity and influence diagnostics, back-calculation, prediction uncertainty and publication-ready base graphics. The workflow is designed to support validation studies rather than rely on a single goodness-of-fit statistic. Methods follow Magnusson and Ornemark (2014) <https://www.eurachem.org/images/stories/Guides/pdf/MV_guide_2nd_ed_EN.pdf> and International Council for Harmonisation (2023) <https://database.ich.org/sites/default/files/ICH_Q2%28R2%29_Guideline_2023_1130_ErrorCorrection_2025.pdf>.

The anota2seq package provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources, translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source. Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels.

iheatmapr is an R package for building complex, interactive heatmaps using modular building blocks. "Complex" heatmaps are heatmaps in which subplots along the rows or columns of the main heatmap add more information about each row or column. For example, a one column additional heatmap may indicate what group a particular row or column belongs to. Complex heatmaps may also include multiple side by side heatmaps which show different types of data for the same conditions. Interactivity can improve complex heatmaps by providing tooltips with information about each cell and enabling zooming into interesting features. iheatmapr uses the plotly library for interactivity.

19 term and 9 first trimester placental chorionic villi and matched cell-sorted samples ran on Illumina HumanMethylationEPIC DNA methylation microarrays. This data was made available on GEO accession [GSE159526](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159526). Both the raw and processed data has been made available on \codeExperimentHub. Raw unprocessed data formatted as an RGChannelSet object for integration and normalization using minfi and other existing Bioconductor packages. Processed normalized data is also available as a DNA methylation \codematrix, with a corresponding phenotype information as a \codedata.frame object.

This package provides estimation procedures for copula-based stochastic frontier quantile models for cross-sectional data. The package implements maximum likelihood estimation of quantile regression models allowing flexible dependence structures between error components through various copula families (e.g., Gaussian and Student-t). It enables estimation of conditional quantile effects, dependence parameters, log-likelihood values, and information criteria (AIC and BIC). The framework combines quantile regression methodology introduced by Koenker and Bassett (1978) <doi:10.2307/1913643> with copula theory described in Joe (2014, ISBN:9781466583221). This approach allows modeling heterogeneous effects across quantiles while capturing nonlinear dependence structures between variables.

This package provides several functions to identify and analyse miRNA sponge, including popular methods for identifying miRNA sponge interactions, two types of global ceRNA regulation prediction methods and four types of context-specific prediction methods( Li Y et al.(2017) <doi:10.1093/bib/bbx137>), which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. In addition, For predictive ceRNA relationship pairs, this package provides several downstream analysis algorithms, including regulatory network analysis and functional annotation analysis, as well as survival prognosis analysis based on expression of ceRNA ternary pair.

Processes tracked points on a fish's body and uses them to estimate standard kinematic parameters such as tail beat amplitude and frequency, body wavelength and wavespeed. As part of this, it also estimates the location of the center of mass and the principal swimming axis. The techniques are described in detail in the main vignette and are published in the book chapter Hawkins, O.H., Di Santo, V., Tytell, Eric.D., 2025. "Biomechanics and energetics of swimming", in: Higham, T.E., Lauder, G.V. (Eds.), Integrative Fish Biomechanics, Fish Physiology. Academic Press. <doi:10.1016/bs.fp.2025.06.003>.

Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. The core fastverse consists of 4 packages: data.table', collapse', kit and magrittr', that jointly only depend on Rcpp'. The fastverse can be freely and permanently extended with additional packages, both globally or for individual projects. Separate package verses can also be created. Fast packages for many common tasks such as time series, dates and times, strings, spatial data, statistics, data serialization, larger-than-memory processing, and compilation of R code are listed in the README file: <https://github.com/fastverse/fastverse#suggested-extensions>.

The official TIMSS 2023 website provides the School Context Data Files for TIMSS 2023 Grade 4 in rdata format. However, the available data are presented solely in the form of numerical values. This package aims to transform the numerical data into categorical data, thereby enabling clearer interpretation and reducing ambiguity in statistical data analysis. Furthermore, the category labels are presented in Bahasa Indonesia. This initiative is intended as a contribution to promoting and expanding the use of Bahasa Indonesia in the field of programming, in line with its designation as one of the official languages of the United Nations General Assembly.