Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) <doi:10.1002/sim.9919> for an overview.

After testing for biased treatment assignment in an observational study using an unaffected outcome, the sensitivity analysis is constrained to be compatible with that test. The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>. The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374 <doi:10.1093/biomet/79.2.367>.

This package provides new imputation methods for the mice package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.

This package implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.

Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.

Running Focused Identification of the Germplasm Strategy (FIGS) to make best subsets from Genebank Collection.

This package provides functions to support the ICES Transparent Assessment Framework <https://taf.ices.dk> to organize data, methods, and results used in ICES assessments. ICES is an organization facilitating international collaboration in marine science.

Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" <doi:10.48550/arXiv.2503.03530>.

Sports Injury Data analysis aims to identify and describe the magnitude of the injury problem, and to gain more insights (e.g. determine potential risk factors) by statistical modelling approaches. The injurytools package provides standardized routines and utilities that simplify such analyses. It offers functions for data preparation, informative visualizations and descriptive and model-based analyses.

This package provides tools for mapping International Classification of Diseases codes to comorbidity, enabling the identification and analysis of various medical conditions within healthcare data.

This package contains functions that allow Bayesian inference on a parameter of some widely-used exponential models. The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019) <doi:10.1007/s42519-018-0027-2>. The package can also be used as a teaching demo for introductory Bayesian courses.

This package contains techniques for mining large and high-dimensional data sets by using the concept of Intrinsic Dimension (ID). Here the ID is not necessarily an integer. It is extended to fractal dimensions. And the Morisita estimator is used for the ID estimation, but other tools are included as well.

This package provides tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0).

Calculate false ring proportions from data frames of intra annual density fluctuations.

Generates Rd files from R source code with comments. The main features of the default syntax are that (1) docs are defined in comments near the relevant code, (2) function argument names are not repeated in comments, and (3) examples are defined in R code, not comments. It is also easy to define a new syntax.

This package contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017, ISBN: 978-1-4200-7747-6) "Survival Analysis with Interval-Censored Data: A Practical Approach" <https://www.routledge.com/Survival-Analysis-with-Interval-Censored-Data-A-Practical-Approach-with/Bogaerts-Komarek-Lesaffre/p/book/9781420077476>. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.

Analysis of the initialization for numerical optimization of real-valued functions, particularly likelihood functions of statistical models. See <https://loelschlaeger.de/ino/> for more details.

The Iterative Cumulative Sum of Squares (ICSS) algorithm by Inclan/Tiao (1994) <https://www.jstor.org/stable/2290916> detects multiple change points, i.e. structural break points, in the variance of a sequence of independent observations. For series of moderate size (i.e. 200 observations and beyond), the ICSS algorithm offers results comparable to those obtained by a Bayesian approach or by likelihood ration tests, without the heavy computational burden required by these approaches.

Density, spectral density, and regression estimation using infinite order flat-top kernels.

This function predicts item response probabilities and item responses using the item-focused tree model. The item-focused tree model combines logistic regression with recursive partitioning to detect Differential Item Functioning in dichotomous items. The model applies partitioning rules to the data, splitting it into homogeneous subgroups, and uses logistic regression within each subgroup to explain the data. Differential Item Functioning detection is achieved by examining potential group differences in item response patterns. This method is useful for understanding how different predictors, such as demographic or psychological factors, influence item responses across subgroups.

This package provides a pipeline to process nominal mass spectrometry data to create .msp files for untargeted analyses.

An R implementation of Matthew Thomas's Python library inteq'. First, this solves Fredholm integral equations of the first kind ($f(s) = \int_a^b K(s, y) g(y) dy$) using methods described by Twomey (1963) <doi:10.1145/321150.321157>. Second, this solves Volterra integral equations of the first kind ($f(s) = \int_0^s K(s,y) g(t) dt$) using methods from Betto and Thomas (2021) <doi:10.48550/arXiv.2106.08496>. Third, this solves Voltera integral equations of the second kind ($g(s) = f(s) + \int_a^s K(s,y) g(y) dy$) using methods from Linz (1969) <doi:10.1137/0706034>.

It performs interlaboratory studies (ILS) to detect those laboratories that provide non-consistent results when comparing to others. It permits to work simultaneously with various testing materials, from standard univariate, and functional data analysis (FDA) perspectives. The univariate approach based on ASTM E691-08 consist of estimating the Mandel's h and k statistics to identify those laboratories that provide more significant different results, testing also the presence of outliers by Cochran and Grubbs tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey tests) to test differences in means corresponding to different laboratories per each material. Taking into account the functional nature of data retrieved in analytical chemistry, applied physics and engineering (spectra, thermograms, etc.). ILS package provides a FDA approach for finding the Mandel's k and h statistics distribution by smoothing bootstrap resampling.

Data from the United States Center for Medicare and Medicaid Services (CMS) is included in this package. There are ICD-9 and ICD-10 diagnostic and procedure codes, and lists of the chapter and sub-chapter headings and the ranges of ICD codes they encompass. There are also two sample datasets. These data are used by the icd package for finding comorbidities.