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>.

Interaction and analysis of multiple response data, along with other tools for analysing these types of data including missing value analysis and calculation of standard errors for a range of covariance matrix results (proportions, multinomial, independent samples, and multiple response).

Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations. See Fox et al. (2024) <doi:10.1016/j.crmeth.2024.100884> for details.

This package provides tools for importing, merging, and analysing data from international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).

This package provides tools for evaluating and ranking missing value imputation methods using proper scoring rules. Implements the Energy-I-Score and the DR-I-Score for the assessment of deterministic, stochastic and multiple imputation methods for numerical and mixed datasets, following Näf et al. (2022) <doi:10.48550/arXiv.2106.03742> and Näf et al. (2025) <doi:10.48550/arXiv.2507.11297>.

An implementation of the iterative bootstrap procedure of Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique.

Nicely formatted frequency tables and contingency tables (1-way, 2-way, 3-way and 4-way tables), that can easily be exported to HTML or Office documents. Designed to work with pipes.

Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data: ICC, Finn-Coefficient, Robinson's A, Kendall's W, Cohen's Kappa, ...

This package provides a toolkit for detecting Differential Item Functioning (DIF) using Logistic Regression (LR) as described in Swaminathan and Rogers (1990) <doi:10.1111/j.1745-3984.1990.tb00754.x>, the IRT Likelihood Ratio Test (LRT) following Thissen, Steinberg & Wainer (1993, ISBN:0-8058-0972-4), and model-based recursive partitioning (MOB) as implemented in strucchange following Strobl, Kopf and Zeileis (2015) <doi:10.1007/s11336-013-9388-3>. Designed for both standard two-group and intersectional multi-group designs, iDIFr prioritises effect size reporting alongside statistical significance, clear guidance on group construction, and interpretable output suitable for applied testing contexts. Built-in Intersectional Contrast Analysis (ICA) classifies items as amplified, pure-intersection, obscured, or none by comparing single-variable and intersectional analyses.

An implementation of generalized linear models (GLMs) for studying relationships among attributes in connected populations, where responses of connected units can be dependent, as introduced by Fritz et al. (2025) <doi:10.1080/01621459.2025.2565851>. igml extends GLMs for independent responses to dependent responses and can be used for studying spillover in connected populations and other network-mediated phenomena.

This package provides functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.

This package provides a systematic framework for integrating multiple modalities of assays profiled on the same set of samples. The goal is to identify genes that are altered in cancer either marginally or consistently across different assays. The heterogeneity among different platforms and different samples are automatically adjusted so that the overall alteration magnitude can be accurately inferred. See Tong and Coombes (2012) <doi:10.1093/bioinformatics/bts561>.

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.

Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.

This package implements imputation methods using EM and Data Augmentation for multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.

Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: Mean', LOCF', Interpolation', Moving Average', Seasonal Decomposition', Kalman Smoothing on Structural Time Series models', Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.

Computes individual causes of death and population cause-specific mortality fractions using the InSilicoVA algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used InterVA method. This package provides general model fitting and customization for InSilicoVA algorithm and basic graphical visualization of the output.

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>.

An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at <doi:10.5201/ipol.2018.229>. The package allows to detect relevant points in images which are characteristic to the digital image.

This package provides a set of tools for processing and analyzing in vitro toxicokinetic measurements in a standardized and reproducible pipeline. The package was developed to perform frequentist and Bayesian estimation on a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The methods provided by the package were described in Wambaugh et al. (2019) <doi:10.1093/toxsci/kfz205>.

Implementation of functions to assign corresponding common job prestige scores (SIOPS, ISEI), the official job or group title and the ISCO-88 code to given ISCO-08 codes. ISCO-08 is the latest version of the International Standard Classification of Occupations which is used to organise information on labour and jobs.

We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).

We consider studies in which information from error-prone diagnostic tests or self-reports are gathered sequentially to determine the occurrence of a silent event. Using a likelihood-based approach incorporating the proportional hazards assumption, we provide functions to estimate the survival distribution and covariate effects. We also provide functions for power and sample size calculations for this setting. Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015) <doi: 10.1214/15-AOAS810>, Xiangdong Gu and Raji Balasubramanian (2016) <doi: 10.1002/sim.6962>, Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes, Yunsheng Ma, and Raji Balasubramanian (2020) <doi: 10.1186/s12911-020-01223-w>.

This package performs Goodness of Fit for regression models using Integrated Regression method. Works for several different fitting techniques.