Perform fast and memory efficient time-weighted averaging of values measured over intervals into new arbitrary intervals. This package is useful in the context of data measured or represented as constant values over intervals on a one-dimensional discrete axis (e.g. time-integrated averages of a curve over defined periods). This package was written specifically to deal with air pollution data recorded or predicted as averages over sampling periods. Data in this format often needs to be shifted to non-aligned periods or averaged up to periods of longer duration (e.g. averaging data measured over sequential non-overlapping periods to calendar years).

Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm (Bock \& Aitkin, 1981 <doi:10.1007/BF02293801>). Within parameter estimation algorithm, several methods for latent distribution estimation are available. Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution.

This package provides functions to make inference about the standardized mortality ratio (SMR) when evaluating the effect of a screening program. The package is based on methods described in Sasieni (2003) <doi: 10.1097/00001648-200301000-00026> and Talbot et al. (2011) <doi: 10.1002/sim.4334>.

Suite of functions to study animal incubation. At the core of incR lies an algorithm that allows for the scoring of incubation behaviour. Additionally, several functions extract biologically relevant metrics of incubation such as off-bout number and off-bout duration - for a review of avian incubation studies, see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.

This package provides a user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) assays in pancreatic beta cells or islets. The package allows the user to import several sets of experiments from different spreadsheets and to perform subsequent steps: summarise in a tidy format, visualise data quality and compare experimental conditions without omitting to account for technical confounders such as the date of the experiment or the technician. Together, insane is a comprehensive method that optimises pre-processing and analyses of GSIS experiments in a friendly-user interface. The Shiny App was initially designed for EndoC-betaH1 cell line following method described in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).

Identifiers in biological databases connect different levels of metadata, phenotype data or genotype data. This tool is designed to easily convert identifiers within or between different biological databases (Wang, Shixiang, et al. (2021) <DOI:10.1371/journal.pgen.1009557>).

Read data from LimeSurvey (<https://www.limesurvey.org/>) in a comfortable way. Heavily inspired by limer (<https://github.com/cloudyr/limer/>), which lacked a few comfort features for me.

This package provides an interface for image recognition using the Google Vision API <https://cloud.google.com/vision/> . Converts API data for features such as object detection and optical character recognition to data frames. The package also includes functions for analyzing image annotations.

This package implements the class of Information-Theoretic (IT) estimators for econometric models, following the unified framework of Golan (2008) <doi:10.1561/0800000004>. Provides Generalized Maximum Entropy (GME) and Generalized Cross-Entropy (GCE) estimators for linear regression, instrumental variables, one-way error-component panel data, multinomial response, matrix balancing, and first-order Markov transition matrices, together with pure and noisy inverse-problem solvers. All estimators use the concentrated (dual) formulation for computational efficiency and report normalized-entropy, entropy-ratio, and Fano-bound diagnostics.

This package provides functions to calculate indices used to score immunoglobulin A (IgA) binding of bacteria in IgA sequencing (IgA-Seq) experiments. This includes the original Kau and Palm indices and more recent methods as described in Jackson et al. (2020) <doi:10.1101/2020.08.19.257501>. Additionally the package contains a function to simulate IgA-Seq data and an example experimental data set for method testing.

This package provides a set of functions for performing null hypothesis testing on samples of persistence diagrams using the theory of permutations. Currently, only two-sample testing is implemented. Inputs can be either samples of persistence diagrams themselves or vectorizations. In the former case, they are embedded in a metric space using either the Bottleneck or Wasserstein distance. In the former case, persistence data becomes functional data and inference is performed using tools available in the fdatest package. Main reference for the interval-wise testing method: Pini A., Vantini S. (2017) "Interval-wise testing for functional data" <doi:10.1080/10485252.2017.1306627>. Main reference for inference on populations of networks: Lovato, I., Pini, A., Stamm, A., & Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>.

Computation of test statistics of independence between (continuous) innovations of time series. They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) <doi:10.1002/cjs.11141>.

Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.

Imputes missing glucose values in repeated-measures continuous glucose monitoring (CGM) data. Workflows create time-series features from raw timestamps, support model selection, and return the user's original columns plus an imputed glucose column. Methods include multiple imputation by chained equations using mice (Azur et al. (2011) <doi:10.1002/mpr.329>), Random Forest regression using ranger (Breiman (2001) <doi:10.1023/A:1010933404324>), k-nearest-neighbor regression using FNN (Zhang (2016) <doi:10.21037/atm.2016.03.37>), XGBoost using xgboost (Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>), LightGBM using lightgbm (Ke et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>), and ARIMA forecasting using forecast (Hyndman and Khandakar (2008) <doi:10.18637/jss.v027.i03>). A Python'-compatible backend uses reticulate to call pandas', scikit-learn', statsmodels', xgboost', and optional lightgbm'.

Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. Immunaut integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations.

Computes individual contributions to the overall Gini and Theil's T and Theil's L measures and their decompositions by groups such as race, gender, national origin, with the three functions of iGini(), iTheiT(), and iTheilL(). For details, see Tim F. Liao (2019) <doi:10.1177/0049124119875961>.

Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.

Fitting and validation of machine learning algorithms for volume prediction of trees, currently for conifer trees based on diameter at breast height and height as explanatory variables.

Collect marketing data from Instagram Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

Interactive shiny application for running Item Response Theory analysis. Provides graphics for characteristic and information curves.

This package implements the Interpolate, Truncate, Project (ITP) root-finding algorithm developed by Oliveira and Takahashi (2021) <doi:10.1145/3423597>. The user provides the function, from the real numbers to the real numbers, and an interval with the property that the values of the function at its endpoints have different signs. If the function is continuous over this interval then the ITP method estimates the value at which the function is equal to zero. If the function is discontinuous then a point of discontinuity at which the function changes sign may be found. The function can be supplied using either an R function or an external pointer to a C++ function. Tuning parameters of the ITP algorithm can be set by the user. Default values are set based on arguments in Oliveira and Takahashi (2021).

This package provides tools to download, style, and insert scalable vector graphics (SVG) icons into R output, such as R Markdown and Quarto documents, Shiny apps, gt tables, and ggplot2 visualisations via the ggicons package. Bundles popular icon libraries, such as Font Awesome (<https://fontawesome.com>), Academicons (<https://jpswalsh.github.io/academicons/>), and Material Design Icons (<https://fonts.google.com/icons>), which are downloaded on demand to keep the package small, or build a custom icon set from any folder of local SVG files. Icon appearance, such as size, colour, and position, can be customised.

This package provides a variety of improved shrinkage estimators in the area of statistical analysis: unrestricted; restricted; preliminary test; improved preliminary test; Stein; and positive-rule Stein. More details can be found in chapter 7 of Saleh, A. K. Md. E. (2006) <ISBN: 978-0-471-56375-4>.

Implementation of tandem clustering with invariant coordinate selection with different scatter matrices and several choices for the selection of components as described in Alfons, A., Archimbaud, A., Nordhausen, K.and Ruiz-Gazen, A. (2024) <doi:10.1016/j.ecosta.2024.03.002>.