Estimation of the required sample size to validate a risk model for binary outcomes, based on the sample size equations proposed by Pavlou et al. (2021) <doi:10.1177/09622802211007522>. For precision-based sample size calculations, the user is required to enter the anticipated values of the C-statistic and outcome prevalence, which can be obtained from a previous study. The user also needs to specify the required precision (standard error) for the C-statistic, the calibration slope and the calibration in the large. The calculations are valid under the assumption of marginal normality for the distribution of the linear predictor.

This package provides computational building blocks for fixed-radius spatial aggregation, weighted circle-placement problems, hotspot detection, and polygon-based spatial summaries. The package focuses on efficient determination of the sum of observations within a given radius, identifying areas of high local concentration, and aggregating point data to polygon geometries. These methods are useful for applications such as insurance, urban analytics, environmental exposure analysis, and other spatial point pattern workflows. The fixed-radius circle placement problem is discussed by Chazelle and Lee (1986) <doi:10.1007/BF02238188>, and related maximum covering problems are described by Church (1974) <doi:10.1007/BF01942293>.

"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().

Implementation of selected Tidyverse functions within DataSHIELD', an open-source federated analysis solution in R. Currently, DataSHIELD contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the server-side package which should be installed on the server holding the data, and is used in conjunction with the client-side package dsTidyverseClient which is installed in the local R environment of the analyst. For more information, see <https://tidyverse.org/> and <https://datashield.org/>.

Person-specific and within-person network estimation from intensive longitudinal and panel data. Estimators include ordinary vector autoregression (VAR), graphical vector autoregression (graphical VAR), multilevel vector autoregression (mlVAR), rolling ordinary and graphical VAR, native Bayesian VAR and multilevel Bayesian VAR, unified Structural Equation Modeling (uSEM), and Group Iterative Multiple Model Estimation (GIMME). All estimators are native clean-room implementations. All functions are validated against authoritative literature. Also provides preprocessing audits, edge-stability diagnostics, model-comparison reports, and rolling forecast validation. Methods are described in <doi:10.1007/978-3-031-95365-1_20> and <doi:10.1080/00273171.2018.1454823>.

This package implements the multivariate classification methods of Alexander Alexandrovich Lyubishchev (1890-1972), as described in his 1943 manuscript Programma obshchey sistematiki Lyubishchev (1943) <https://www.zin.ru/animalia/coleoptera/rus/lyubis05.htm> and published in Lubischew (1962) <https://www.jstor.org/stable/2527894>. Provides divergence_coefficient() for measuring separation between groups on continuous features, scatter_ellipse() for fitting covariance ellipses per class, transgression() for detecting ellipse overlap, and classify() for Bayesian posterior classification. These methods predate and are more general than the binary-character similarity coefficients of Sokal and Sneath (1963) that appear in other R packages.

This package provides a family of three complementary goodness-of-fit tests based on an adaptation of Hotelling's T-squared statistic applied to vectors of sample log-cumulants (Mellin statistics) for positive-support reliability data. The package provides the asymptotic chi-squared reference and parametric bootstrap p-values for reliable finite-sample inference, covering the Weibull, Frechet, Gamma, Inverse-Gamma, Log-Normal, and Log-Logistic families. It also provides three diagnostic diagrams (log-cumulant, kurtosis-skewness, and coefficient-of-variation) with bootstrap concentration ellipses, in the spirit of moment-ratio diagrams. Methods are described in Santos, Ospina, Espinheira and Oliveira (2025).

This package performs a multiscale analysis of a nonparametric regression or nonparametric regressions with time series errors. In case of one regression, with the help of this package it is possible to detect the regions where the trend function is increasing or decreasing. In case of multiple regressions, the test identifies regions where the trend functions are different from each other. See Khismatullina and Vogt (2020) <doi:10.1111/rssb.12347>, Khismatullina and Vogt (2022) <doi:10.48550/arXiv.2209.10841> and Khismatullina and Vogt (2023) <doi:10.1016/j.jeconom.2021.04.010> for more details on theory and applications.

This package implements several methods to estimate effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. Specifically, the package implements the time-smoothed inverse probability weighted estimators described in McGrath et al. (2025) <doi:10.48550/arXiv.2509.13971>. Outcomes may be repeatedly, non-monotonically, informatively, and sparsely measured in the data source. The package also supports settings where outcomes are truncated by death, i.e. some individuals die during follow-up which renders the outcome of interest undefined at the follow-up time of interest.

Facilitate the analysis of inter-limb and intra-limb coordination in human movement. It provides functions for calculating the phase angle between two segments, enabling researchers and practitioners to quantify the coordination patterns within and between limbs during various motor tasks. Needham, R., Naemi, R., & Chockalingam, N. (2014) <doi:10.1016/j.jbiomech.2013.12.032>. Needham, R., Naemi, R., & Chockalingam, N. (2015) <doi:10.1016/j.jbiomech.2015.07.023>. Tepavac, D., & Field-Fote, E. C. (2001) <doi:10.1123/jab.17.3.259>. Park, J.H., Lee, H., Cho, Js. et al. (2021) <doi:10.1038/s41598-020-80237-w>.

The MSstatsLOBD package allows calculation and visualization of limit of blac (LOB) and limit of detection (LOD). We define the LOB as the highest apparent concentration of a peptide expected when replicates of a blank sample containing no peptides are measured. The LOD is defined as the measured concentration value for which the probability of falsely claiming the absence of a peptide in the sample is 0.05, given a probability 0.05 of falsely claiming its presence. These functionalities were previously a part of the MSstats package. The methodology is described in Galitzine (2018) <doi:10.1074/mcp.RA117.000322>.

Fits and interprets mixed-effects models for clustered, longitudinal and heterogeneous biological data. Provides variance partitioning, intraclass correlation, penalized likelihood summaries, a heterogeneous-data information criterion, model comparison, diagnostics, and ensemble-style summaries for multilevel data. The package is designed as a complementary, interpretable workflow around established mixed-model methods. Methods for intraclass correlation and variance partitioning are informed by Nakagawa and Schielzeth (2010) <doi:10.1111/j.1469-185X.2010.00141.x> and Nakagawa et al. (2017) <doi:10.1098/rsif.2017.0213>. Mixed-effects modeling approaches are described by Zuur et al. (2009) <doi:10.1007/978-0-387-87458-6>.

This package provides a tool developed with the Golem framework which provides an easier way to check cells differences between two data frames. The user provides two data frames for comparison, selects IDs variables identifying each row of input data, then clicks a button to perform the comparison. Several R package functions are used to describe the data and perform the comparison in the server of the application. The main ones are comparedf() from arsenal and skim() from skimr'. For more details see the description of comparedf() from the arsenal package and that of skim() from the skimr package.

This package provides tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2020a, <doi:10.48550/arXiv.1804.04916>) and Cattaneo, Farrell and Feng (2020b, <doi:10.48550/arXiv.1906.00202>): lsprobust() for nonparametric point estimation of regression functions and their derivatives and for robust bias-corrected (pointwise and uniform) inference; lspkselect() for data-driven selection of the IMSE-optimal number of knots; lsprobust.plot() for regression plots with robust confidence intervals and confidence bands; lsplincom() for estimation and inference for linear combinations of regression functions from different groups.

This package provides a shiny web application to map scores from clinical instruments (PANSS, SQLS, WHODAS 2.0, PHQ-8, EQ-5D-5L) to preference-based EQ-5D-5L health utility values using validated regression-based and beta-mixture mapping algorithms developed from Singapore population studies. Intended for use in health economic evaluations and cost-utility analyses. Methods are based on: Abdin et al. (2019) <doi:10.1007/s11136-018-2037-7>, Seow et al. (2023) <doi:10.1080/14737167.2023.2215430>, Abdin et al. (2021) <doi:10.1186/s12888-021-03463-0>, Abdin et al. (2024) <doi:10.1080/14737167.2024.2376100>.

Computes A-, MV-, D- and E-optimal or near-optimal row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all pairwise treatment contrasts. The algorithms used in this package are based on the array exchange and treatment exchange algorithms adopted from Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> algorithms after adjusting for the row-column designs setup. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.

Dates sediment cores from lead-210 (Pb-210) activity profiles measured by alpha or gamma spectrometry, following the unified formulation and nomenclature of Sanchez-Cabeza and Ruiz-Fernandez (2012) <doi:10.1016/j.gca.2010.12.024>. Implements the Constant Flux (CF) and Constant Flux Constant Sedimentation (CFCS) dating models, together with supporting tools for data input, decay correction, missing inventory estimation, calculation of sediment and mass accumulation rates, and Monte Carlo propagation of dating uncertainties as described in Sanchez-Cabeza et al. (2014) <doi:10.1016/j.quageo.2014.06.002>. Also provides functions to visualize activity profiles and resulting age models.

Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: data', a dataframe containing player data for any sports; player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; name_col', specifying the name of the column in the dataframe that contains the player names; and player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title.

This package implements the ThemeScope framework for detecting and visualising social representations in large-scale digital text corpora. From raw documents it builds, via udpipe annotation, sentence-level word co-occurrence networks and derives two community-level indicators grounded in Social Representation Theory: the Prototypical Salience Index (PSI) for anchoring and the Concreteness Score (CS) for objectification. Communities are located in a two-dimensional, theoretically grounded representational map. The whole pipeline is usable from the R console; an optional shiny graphical interface calls the same exported functions. The method is described in Misuraca, Spano and D'Aniello (2026) <doi:10.1177/01655515261454276>.

Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data. See the vignette for more details on how to use this package, some illustrations, and simple examples.

Manipulation of catastrophe model outputs, including tasks such as simulating year loss tables (YLTs) from event loss tables (ELTs), adjusting the frequencies of events in YLTs to create new YLTs, applying catastrophe exceedance of loss contracts (catXL), applying hours clauses, and calculating diagnostics from ELTs and YLTs, such as average annual loss and exceedance probability curves. Frequency adjustment routines are based on the paper "A new simulation algorithm for more precise estimates of change in catastrophe risk models, with application to hurricanes and climate change", Jewson, S. (2023); <doi:10.1007/s00477-023-02409-0>. Version 0.0.2 no longer uses rust.

Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained. The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc. The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity. Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>. Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>.

This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.

This package provides a flexible and easy-to use interface for the soil vegetation atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran. The model simulates daily transpiration, interception, soil and snow evaporation, streamflow and soil water fluxes through a soil profile covered with vegetation, as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) <doi:10.1175/1525-7541(2003)004%3C1276:SOAETS%3E2.0.CO;2>. A set of high-level functions for model set up, execution and parallelization provides easy access to plot-level SVAT simulations, as well as multi-run and large-scale applications.