This package contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.

This package implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) <doi:10.1007/s11222-020-09940-y>.

Fits cumulative link models (CLMs) for ordinal categorical data using CmdStanR'. Supports various link functions including logit, probit, cloglog, loglog, cauchit, and flexible parametric links such as Generalized Extreme Value (GEV), Asymmetric Exponential Power (AEP), and Symmetric Power. Models are pre-compiled using the instantiate package for fast execution without runtime compilation. Methods are described in Agresti (2010, ISBN:978-0-470-08289-8), Wang and Dey (2011) <doi:10.1007/s10651-010-0154-8>, and Naranjo, Perez, and Martin (2015) <doi:10.1007/s11222-014-9449-1>.

This package provides a minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis.

Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. This package provides functions for processing and visualizing concept mapping data, involving the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. The methodology and its validity are discussed in Kampen, J.K., Hageman, J.A., Breuer, M., & Tobi, H. (2025). "The validity of concept mapping: let's call a spade a spade." Qual Quant. <doi:10.1007/s11135-025-02351-z>.

Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps non-linear. This package implements methods for understanding the division of space between the groups.

This package provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.

Designs guide sequences for CRISPR/Cas9 genome editing and provides information on sequence features pertinent to guide efficiency. Sequence features include annotated off-target predictions in a user-selected genome and a predicted efficiency score based on the model described in Doench et al. (2016) <doi:10.1038/nbt.3437>. Users are able to import additional genomes and genome annotation files to use when searching and annotating off-target hits. All guide sequences and off-target data can be generated through the R console with sgRNA_Design() or through crispRdesignR's user interface with crispRdesignRUI(). CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) and the associated protein Cas9 refer to a technique used in genome editing.

An interactive shiny training companion for people who are new to data, developed for World Health Organization data trainings. Bundles hands-on modules for importing data, missing values, outliers, text cleaning, merging, visualization, and basic statistics, plus a set of AI-skills pages (prompting levels, a prompt gallery, AI safety rules, and a methodology case study) and an interactive quiz with per-session topic filters. Launch the app with run_cata().

Map functions while capturing results, errors, warnings, messages and other output tidily, then filter and summarise data frames or lists on the basis of those side effects.

We aim to deal with the average treatment effect (ATE), where the data are subject to high-dimensionality and measurement error. This package primarily contains two functions, which are used to generate artificial data and estimate ATE with high-dimensional and error-prone data accommodated.

Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.

Computes the cosine-correlation coefficient for measuring the degree of linear dependence among variables in a multidimensional context. The package implements the generalized cosine-correlation theorem for p-1 variables, providing a quantitative assessment of interrelationships within experimental frameworks. This methodology extends classical correlation measures to higher-dimensional spaces using a dimensional exploration approach based on time scale calculus.

Computes conditional multivariate t probabilities, random deviates, and densities. It can also be used to create missing values at random in a dataset, resulting in a missing at random (MAR) mechanism. Inbuilt in the package are the Expectation-Maximization (EM), Monte Carlo EM, and Stochastic EM algorithms for imputation of missing values in datasets assuming the multivariate t distribution. See Kinyanjui, Tamba, Orawo, and Okenye (2020)<doi:10.3233/mas-200493>, and Kinyanjui, Tamba, and Okenye(2021)<http://www.ceser.in/ceserp/index.php/ijamas/article/view/6726/0> for more details.

ActiGraph wGT3X-BT accelerometer processing methods using the standardized workflow developed by Statistics Canada for cycle 7 of the Canadian Health Measures Survey (CHMS). The package promotes transparent and reproducible data processing while supporting the harmonization of analytical approaches among researchers wishing to align with Statistics Canada's methods. For general details about the processing methods, please consult Clarke J, Gribbon A, St-Laurent M, Ferrao T, Barnes J, Kuzik N, Colley R (2026) <doi: 10.25318/82-003-x202600200001-eng>.

This package provides quantitative tools for substantive and content-oriented scale pretesting. Implements item-sort indices from Anderson and Gerbing (1991) <doi:10.1037/0021-9010.76.5.732>, exact item-sort inference following Howard and Melloy (2016) <doi:10.1007/s10869-015-9404-y>, empirical interpretation benchmarks from Colquitt et al. (2019) <doi:10.1037/apl0000406>, and the construct-rating procedure of Hinkin and Tracey (1999) <doi:10.1177/109442819922004> with HTC/HTD indices and repeated-measures item screening. The expert-panel workflow combines Aiken's V with score confidence intervals, Lawshe content validity ratios with exact inference, content validity indices with modified kappa and score intervals, item-objective congruence, and panel-level agreement using Krippendorff's alpha as described by Hayes and Krippendorff (2007) <doi:10.1080/19312450709336664>. Also provides judge and rater heterogeneity analysis following the generalizability-theory treatment of content-validity ratings in Crocker, Llabre and Miller (1988) <doi:10.1111/j.1745-3984.1988.tb00309.x>, content-domain coverage and expert-perceived content structure following Sireci and Geisinger (1992) <doi:10.1177/014662169201600102>, comparison across successive pretest rounds, and exact expert-panel planning. Where published methods compete, users choose among them through arguments with evidence-based defaults. User-facing workflows emphasize interpretable summaries and transparent review recommendations rather than isolated coefficients.

This package provides a simple way to manage application settings by loading configuration values from .env or .ini files. It supports default values, type casting, and environment variable overrides, enabling a clean separation of configuration from code. Ideal for managing credentials, API keys, and deployment-specific settings.

Evaluation for density and distribution function of convolution of gamma distributions in R. Two related exact methods and one approximate method are implemented with efficient algorithm and C++ code. A quick guide for choosing correct method and usage of this package is given in package vignette. For the detail of methods used in this package, we refer the user to Mathai(1982)<doi:10.1007/BF02481056>, Moschopoulos(1984)<doi:10.1007/BF02481123>, Barnabani(2017)<doi:10.1080/03610918.2014.963612>, Hu et al.(2020)<doi:10.1007/s00180-019-00924-9>.

This package provides a convenient tool to store and format browser cookies and use them in HTTP requests (for example, through httr2', httr or curl').

This package provides a collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) <doi:10.1093/bioinformatics/btv205>.

Enable the use of Shepherd.js to create tours in Shiny applications.

Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.

This package provides function declarations and inline function definitions that facilitate communication between R and the Armadillo C++ library for linear algebra and scientific computing. This implementation is detailed in Vargas Sepulveda and Schneider Malamud (2024) <doi:10.1016/j.softx.2025.102087>.

This package provides tools for fitting, assessing, and comparing logistic and conditional logistic regression models. Includes residual diagnostics and goodness of fit measures for model development and evaluation in matched case control studies.