This package implements statistical inference for systems of ordinary differential equations, that uses the integral-matching criterion and takes advantage of the separability of parameters, in order to obtain initial parameter estimates for nonlinear least squares optimization. Dattner & Yaari (2018) <arXiv:1807.04202>. Dattner et al. (2017) <doi:10.1098/rsif.2016.0525>. Dattner & Klaassen (2015) <doi:10.1214/15-EJS1053>.

Handling and manipulation polygons, coordinates, and other geographical objects. The tools include: polygon areas, barycentric and trilinear coordinates (Hormann and Floater, 2006, <doi:10.1145/1183287.1183295>), convex hull for polygons (Graham and Yao, 1983, <doi:10.1016/0196-6774(83)90013-5>), polygon triangulation (Toussaint, 1991, <doi:10.1007/BF01905693>), great circle and geodesic distances, Hausdorff distance, and reduced major axis.

Palettes generated from Tintin covers. There is one palette per cover, with a total of 24 palettes of 5 colours each. Includes functions to interpolate colors in order to create more colors based on the provided palettes.The data is based on Cyr, et al. (2004) <doi:10.1503/cmaj.1041405> and Wikipedia <https://en.wikipedia.org/wiki/The_Adventures_of_Tintin>.

Multinomial (inverse) regression inference for text documents and associated attributes. For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included. Note that the topic modeling capability of earlier textir is now a separate package, maptpx'.

Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.

This package provides inference for the Wilcoxon-Mann-Whitney test under the null hypothesis H0: AUC = 0.5 for continuous, discrete or mixed random variables. Traditional implementations test H0: F = G, which is inappropriately broad and leads to erroneous inferences. Methods are described in M. Grendar (2025) "Wilcoxon-Mann-Whitney Test of No Group Discrimination" <doi:10.48550/arXiv.2511.20308>.

Implementation of various spirometry equations in R, currently the GLI-2012 (Global Lung Initiative; Quanjer et al. 2012 <doi:10.1183/09031936.00080312>), the race-neutral GLI global 2022 (Global Lung Initiative; Bowerman et al. 2023 <doi:10.1164/rccm.202205-0963OC>), the NHANES3 (National Health and Nutrition Examination Survey; Hankinson et al. 1999 <doi:10.1164/ajrccm.159.1.9712108>) and the JRS 2014 (Japanese Respiratory Society; Kubota et al. 2014 <doi:10.1016/j.resinv.2014.03.003>) equations. Also the GLI-2017 diffusing capacity equations <doi:10.1183/13993003.00010-2017> are implemented. Contains user-friendly functions to calculate predicted and LLN (Lower Limit of Normal) values for different spirometric parameters such as FEV1 (Forced Expiratory Volume in 1 second), FVC (Forced Vital Capacity), etc, and to convert absolute spirometry measurements to percent (%) predicted and z-scores.

This is a package for fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one color dimension). It provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analyzing image data using R. The package wraps CImg, a simple, modern C++ library for image processing.

Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.

Facilitates writing computationally reproducible student theses in PDF format that conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). The package currently provides two R Markdown templates for homework and theses at the Psychology Department of the University of Cologne. The package builds on the package papaja but is tailored to the requirements of student theses and omits features for simplicity.

Asymptotic simultaneous confidence intervals for comparison of many treatments with one control, for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals. Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size iteration based on approximate any-pair power. Exact conditional maximum test for many-to-one comparisons to a control.

Search, query, and download tabular and geospatial data from the British Columbia Data Catalogue (<https://catalogue.data.gov.bc.ca/>). Search catalogue data records based on keywords, data licence, sector, data format, and B.C. government organization. View metadata directly in R, download many data formats, and query geospatial data available via the B.C. government Web Feature Service ('WFS') using dplyr syntax.

This package performs brace expansions on strings. Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string. The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.

Automatic specification and estimation of reserve demand curves for central bank operations. The package can help to choose the best demand curve and identify additional explanatory variables. Various plot and predict options are included. For more details, see Chen et al. (2023) <https://www.imf.org/en/Publications/WP/Issues/2023/09/01/Modeling-the-Reserve-Demand-to-Facilitate-Central-Bank-Operations-538754>.

Calculate various cardiovascular disease risk scores from the Framingham Heart Study (FHS), the American College of Cardiology (ACC), and the American Heart Association (AHA) as described in Dâ agostino, et al (2008) <doi:10.1161/circulationaha.107.699579>, Goff, et al (2013) <doi:10.1161/01.cir.0000437741.48606.98>, and Mclelland, et al (2015) <doi:10.1016/j.jacc.2015.08.035>.

This package contains a single function dclust() for divisive hierarchical clustering based on recursive k-means partitioning (k = 2). Useful for clustering large datasets where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records). For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.

Dynamic treatment regime estimation and inference via G-estimation, dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference via bootstrap and recursive sandwich estimation. Estimation and inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). Extension to continuous treatment variables. Wallace et al. (2017) <DOI:10.18637/jss.v080.i02>; Simoneau et al. (2020) <DOI:10.1080/00949655.2020.1793341>.

This package provides a collection of datasets essential for functional genomic analysis. Gene names, gene positions, cytoband information, sourced from Ensembl and phenotypes association graph prepared from GWAScatalog are included. Data is available in both GRCh37 and 38 builds. These datasets facilitate a wide range of genomic studies, including the identification of genetic variants, exploration of genomic features, and post-GWAS functional analysis.

This package implements the Fourier cumulative sum (CUSUM) cointegration test for detecting cointegration relationships in time series data with structural breaks. The test uses Fourier approximations to capture smooth structural changes and CUSUM statistics to test for cointegration stability. Based on methodology described in Zaghdoudi (2025) <doi:10.46557/001c.144076>. The corrected Akaike Information Criterion (AICc) is used for optimal frequency selection.

Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.

Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices.

An approach to analyzing Likert response items, with an emphasis on visualizations. The stacked bar plot is the preferred method for presenting Likert results. Tabular results are also implemented along with density plots to assist researchers in determining whether Likert responses can be used quantitatively instead of qualitatively. See the likert(), summary.likert(), and plot.likert() functions to get started.

The proposed method aims at predicting the longitudinal mean response trajectory by a kernel-based estimator. The kernel estimator is constructed by imposing weights based on subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. Users could also perform variable selections to derive functional predictors with predictive significance by the proposed multiplicative model with multivariate Gaussian kernels.

The goal of McMiso is to provide functions for isotonic regression when there are multiple independent variables. The functions solve the optimization problem using recursion and leverage parallel computing to improve speed, and are useful for situations with relatively large number of covariates. The estimation method follows the projective Bayes solution described in Cheung and Diaz (2023) <doi:10.1093/jrsssb/qkad014>.