Download, cache, and manage social contact survey data from the social contact data community on Zenodo (<https://zenodo.org/communities/social_contact_data>) for use in infectious disease modelling. Provides functions to list available surveys, download survey files with automatic caching, and retrieve citations. Contact survey data describe who contacts whom in a population and are used to parameterise age-structured transmission models, for example via the socialmixr package. The surveys available include those from the POLYMOD study (Mossong et al. (2008) <doi:10.1371/journal.pmed.0050074>) and other social contact data shared on Zenodo.

Calculates population attributable fraction causal effects. The causalPAF package contains a suite of functions for causal analysis calculations of population attributable fractions (PAF) given a causal diagram which apply both: Pathway-specific population attributable fractions (PS-PAFs) Oâ Connell and Ferguson (2022) <doi:10.1093/ije/dyac079> and Sequential population attributable fractions Ferguson, Oâ Connell, and Oâ Donnell (2020) <doi:10.1186/s13690-020-00442-x>. Results are presentable in both table and plot format.

Augment clinical data with metadata to create output used in conventional publications and reports.

It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.

Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>.

Selection of the number of clusters in cluster analysis using stability methods.

Create Pairwise Comparison Matrices for use in the Analytic Hierarchy Process. The Pairwise Comparison Matrix created will be a logical matrix, which unlike a random comparison matrix, is similar to what a rational decision maker would create on the basis of a preference vector for the alternatives considered.

Designed for web usage data analysis, it implements tools to process web sequences and identify web browsing profiles through sequential classification. Sequences clusters are identified by using a model-based approach, specifically mixture of discrete time first-order Markov models for categorical web sequences. A Bayesian approach is used to estimate model parameters and identify sequences classification as proposed by Fruehwirth-Schnatter and Pamminger (2010) <doi:10.1214/10-BA606>.

This package provides functions to generate ensembles of generalized linear models using competing proximal gradients. The optimal sparsity and diversity tuning parameters are selected via an alternating grid search.

This package provides a cascade select widget for usage in Shiny applications. This is useful for selection of hierarchical choices (e.g. continent, country, city). It is taken from the JavaScript library PrimeReact'.

Plot confidence interval from the objects of statistical tests such as t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest class), Tukey test [TukeyHSD()], Dunnett test [glht() in multcomp package], logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in userfriendlyscience package]. Users are able to set the styles of lines and points. This package contains the function to calculate odds ratios and their confidence intervals from the result of logistic regression.

Calculate date of birth, age, and gender, and generate anonymous sequence numbers from CPR numbers. <https://en.wikipedia.org/wiki/Personal_identification_number_(Denmark)>.

Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) <doi:10.1002/sim.1101> The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) <doi:10.1111/biom.13361>.

This package provides an interactive shiny web application for constructing, analyzing, and visualizing composite indices from multidimensional datasets. Users can upload or select indicator data, group variables into logical categories, apply normalization and weighting methods (such as equal or custom schemes), and compute aggregate composite indices. The shiny interface includes tools for exploring results through tables, plots, and data exports, making it useful for researchers, policymakers, and analysts interested in index-based evaluations.

Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) <doi:10.1126/science.1132939>. It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot.

Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.

To calculate the AQI (Air Quality Index) from pollutant concentration data. O3, PM2.5, PM10, CO, SO2, and NO2 are available currently. The method can be referenced at Environmental Protection Agency, United States as follows: EPA (2016) <https://www3.epa.gov/airnow/aqi-technical-assistance-document-may2016.pdf>.

Colorful Data Frames in the terminal. The new class does change the behaviour of any of the objects, but adds a style definition and a print method. Using ANSI escape codes, it colors the terminal output of data frames. Some column types (such as p-values and identifiers) are automatically recognized.

Agreement of continuously scaled measurements made by two techniques, devices or methods is usually evaluated by the well-established Bland-Altman analysis or plot. Conditional method agreement trees (COAT), proposed by Karapetyan, Zeileis, Henriksen, and Hapfelmeier (2025) <doi:10.1093/jrsssc/qlae077>, embed the Bland-Altman analysis in the framework of recursive partitioning to explore heterogeneous method agreement in dependence of covariates. COAT can also be used to perform a Bland-Altman test for differences in method agreement.

Call the DeOldify <https://github.com/jantic/DeOldify> image colorization API on DeepAI'<https://deepai.org/machine-learning-model/colorizer> to colorize black and white images.

Quickly estimate the net growth rate of a population or clone whose growth can be approximated by a birth-death branching process. Input should be phylogenetic tree(s) of clone(s) with edge lengths corresponding to either time or mutations. Based on coalescent results in Johnson et al. (2023) <doi:10.1093/bioinformatics/btad561>. Simulation techniques as well as growth rate methods build on prior work from Lambert A. (2018) <doi:10.1016/j.tpb.2018.04.005> and Stadler T. (2009) <doi:10.1016/j.jtbi.2009.07.018>.

This package performs classical age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010) <doi:10.1016/j.quageo.2010.01.002>.

This package provides Capital Budgeting Analysis functionality and the essential Annuity loan functions. Also computes Loan Amortization Schedules including schedules with irregular payments.

Allows one to assess the stability of individual objects, clusters and whole clustering solutions based on repeated runs of the K-means and K-medoids partitioning algorithms.