Miscellaneous functions for (1) data handling (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., bootstrap confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with Blimp and Mplus'.

Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow.

Estimation/multiple imputation programs for mixed categorical and continuous data.

This package provides an RStudio extension with a chat interface for an AI coding agent to help users with R programming tasks.

Local linear estimation of psychometric functions. Provides functions for nonparametric estimation of a psychometric function and for estimation of a derived threshold and slope, and their standard deviations and confidence intervals.For more details see Zychaluk and Foster (2009) <doi:10.3758/APP.71.6.1414> and Foster and Zychaluk (2007) <doi:10.1109/MSP.2007.4286564>.

Compute the average of a sequence of random vectors in a moving expanding window using a fixed amount of memory.

Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions. Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the Eigen library for linear algebra. Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations.

Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.

This package provides a range of functions for computing both global and local mark correlation functions for spatial point patterns in either Euclidean spaces or on linear networks, with points carrying either real-valued or function-valued marks. For a review of mark correlation functions, see Eckardt and Moradi (2024) <doi:10.1007/s13253-024-00605-1>.

Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references are Royston and Altman (1994) <doi:10.2307/2986270> and Royston and Sauerbrei (2008, ISBN:978-0-470-02842-1). In addition, it can model a sigmoid relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) <doi:10.1177/1536867X1401400206>. This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling.

This package provides methods for analyzing and using quartets displayed on a collection of gene trees, primarily to make inferences about the species tree or network under the multi-species coalescent model. These include quartet hypothesis tests for the model, as developed by Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, simplex plots of quartet concordance factors as presented by Allman et al. (2020) <doi:10.1101/2020.02.13.948083>, species tree inference methods based on quartet distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and Yourdkhani and Rhodes (2019) <doi:10.1007/s11538-020-00773-4>, the NANUQ algorithm for inference of level-1 species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>, the TINNIK algorithm for inference of the tree of blobs of an arbitrary network of Allman et al.(2022) <doi:10.1007/s00285-022-01838-9>, and NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in Allman et al.(2024) (forthcoming). Software announcement by Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.

This package provides a method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.

This package provides programmatic access to the Meetup GraphQL API (<https://www.meetup.com/graphql/>), enabling users to retrieve information about groups, events, and members from Meetup (<https://www.meetup.com/>). Supports authentication via OAuth2 and includes functions for common queries and data manipulation tasks.

Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.

Mobile-monitoring or "sensors on a mobile platform", is an increasingly popular approach to measure high-resolution pollution data at the street level. Coupled with location data, spatial visualisation of air-quality parameters helps detect localized areas of high air-pollution, also called hotspots. In this approach, portable sensors are mounted on a vehicle and driven on predetermined routes to collect high frequency data (1 Hz). mmaqshiny is for analysing, visualising and spatial mapping of high-resolution air-quality data collected by specific devices installed on a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate matter with size less than 2.5 microns), Black carbon mass concentrations (BC), ultra-fine particle number concentrations, carbon dioxide along with GPS coordinates and relative humidity (RH) data collected by popular portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It incorporates device specific cleaning and correction algorithms. RH correction is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) <doi:10.1016/j.atmosenv.2004.03.007>. Provision is given to add linear regression coefficients for correcting the PM2.5 data (if required). BC data will be cleaned for the vibration generated noise, by adopting the statistical procedure as explained in Apte et al., (2011) <doi:10.1016/j.atmosenv.2011.05.028>, followed by a loading correction as suggested by Ban-Weiss et al., (2009) <doi:10.1021/es8021039>. For the number concentration data, provision is given for dilution correction factor (if a diluter is used with CPC3007; default value is 1). The package joins the raw, cleaned and corrected data from the above said instruments and outputs as a downloadable csv file.

Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. It implements the Clinical Laboratory Standard International (CLSI) recommendations (see J. A. Budd et al. (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) for analytical method comparison and bias estimation using patient samples. Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>). Further the robust M-Deming and MM-Deming (experimental) are available, see G. Pioda (2021, <arXiv:2105:04628>). A comprehensive overview over the implemented methods and references can be found in the manual pages mcrPioda-package and mcreg'.

Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; mice', described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; missForest', described by Stekhoven and Buhlmann (2012) <doi:10.1093/bioinformatics/btr597>; missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind missCompare is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. missCompare takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. missCompare will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.

Implementation of adaptive assessment procedures based on Knowledge Space Theory (KST, Doignon & Falmagne, 1999 <ISBN:9783540645016>) and Formal Psychological Assessment (FPA, Spoto, Stefanutti & Vidotto, 2010 <doi:10.3758/BRM.42.1.342>) frameworks. An adaptive assessment is a type of evaluation that adjusts the difficulty and nature of subsequent questions based on the test taker's responses to previous ones. The package contains functions to perform and simulate an adaptive assessment. Moreover, it is integrated with two Shiny interfaces, making it both accessible and user-friendly. The package has been partially funded by the European Union - NextGenerationEU and by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.5, project â RAISE - Robotics and AI for Socio-economic Empowermentâ (ECS00000035).

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.

Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test, Royston test and Henze-Zirkler test.

Calibrate and apply multivariate bias correction algorithms for climate model simulations of multiple climate variables. Three methods described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented â (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr), and (iii) MBC N-dimensional PDF transform (MBCn) â as is the Rank Resampling for Distributions and Dependences (R2D2) method.

An RStudio Addin wrapper for the mergen package. This package employs artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. This package makes it easier to use Large Language Models in your development environment by providing a chat-like interface, while also allowing you to inspect and execute the returned code.

Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.

Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC() function); - transforming the marker by a proper function trying to improve the classification performance (hROC() function); - when dealing with multivariate markers, considering a proper transformation to univariate space trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function). The classification regions behind each point of the ROC curve are displayed in both static graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or videos (movieROC() function).