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>.

This package contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).

This package implements the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2019) <arXiv:1901.04599>.

Network functionalities specialized for data generated from input-output tables.

Estimates quantile-based inequality indicators from complex survey data, including the quantile ratio index (QRI), quintile share Ratio (QSR), Palma ratio, and percentile ratios, together with the Gini coefficient. Influence functions are provided for linearization and variance estimation, along with a rescaled bootstrap for complex sampling designs. Estimation from grouped data is also supported. See Scarpa et al. (2025) <doi:10.1093/jssam/smaf024> for details.

Allows for the non-parametric estimation of transition intensities in interval-censored multi-state models using the approach of Gomon and Putter (2024) <doi:10.48550/arXiv.2409.07176> or Gu et al. (2023) <doi:10.1093/biomet/asad073>.

Intervention analysis is used to investigate structural changes in data resulting from external events. Traditional time series intervention models, viz. Autoregressive Integrated Moving Average model with exogeneous variables (ARIMA-X) and Artificial Neural Networks with exogeneous variables (ANN-X), rely on linear intervention functions such as step or ramp functions, or their combinations. In this package, the Gompertz, Logistic, Monomolecular, Richard and Hoerl function have been used as non-linear intervention function. The equation of the above models are represented as: Gompertz: A * exp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) * exp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K - A) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl: a*(b^t)*(t^c).This package introduced algorithm for time series intervention analysis employing ARIMA and ANN models with a non-linear intervention function. This package has been developed using algorithm of Yeasin et al. <doi:10.1016/j.hazadv.2023.100325> and Paul and Yeasin <doi:10.1371/journal.pone.0272999>.

Creation of tables of summary statistics or counts for clinical data (for TLFs'). These tables can be exported as in-text table (with the flextable package) for a Clinical Study Report (Word format) or a topline presentation (PowerPoint format), or as interactive table (with the DT package) to an html document for clinical data review.

Implementation of the classifier described in the paper Ali HR et al (2014) <doi:10.1186/s13059-014-0431-1>. It uses copy number and/or expression form breast cancer data, trains a Tibshirani's pamr classifier with the features available and predicts the iC10 group.

This package infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).

This package provides a fresh take on iterators in R. Designed to be cross-compatible with the iterators package, but using the nextOr method will offer better performance as well as more compact code. With batteries included: includes a collection of iterator constructors and combinators ported and refined from the iterators', itertools', and itertools2 packages.

Dichotomous and polytomous data analysis and their scoring using the unidimensional Item Response Theory model (Chalmers (2012) <doi:10.18637/jss.v048.i06>) with user-friendly graphic User Interface. Suitable for beginners who are learning item response theory.

This package provides methods for testing the equality of dependent intraclass correlation coefficients (ICCs) estimated using linear mixed-effects models. Several of the implemented approaches are based on the work of Donner and Zou (2002) <doi:10.1111/1467-9884.00324>.

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>.

Generates a Graphviz graph of the most significant 3-way interaction gains (i.e. conditional information gains) based on a provided discrete data frame. Various output formats are supported ('Graphviz', SVG, PNG, PDF, PS). For references, see the webpage of Aleks Jakulin <http://stat.columbia.edu/~jakulin/Int/>.

Call the data wrappers for Izmir Metropolitan Municipality's Open Data Portal. This will return all datasets formatted as Excel files (.csv or .xlsx), as well as datasets that require an API key.

This R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on Koo and Pashley (2026) <doi:10.1093/biomet/asag013>, it includes block-level estimators and extends to unit-level effects using Horvitz-Thompson and Hájek estimators. The package also provides asymptotic confidence intervals to support valid statistical inference.

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

When you want to install R package or download file from GitHub, but you can't access GitHub, this package helps you install R packages or download file from GitHub via the proxy website <https://gh-proxy.com/> or <https://ghfast.top/>, which is in real-time sync with GitHub.

Estimation of the most-left informative set of gross returns (i.e., the informative set). The procedure to compute the informative set adjusts the method proposed by Mariani et al. (2022a) <doi:10.1007/s11205-020-02440-6> and Mariani et al. (2022b) <doi:10.1007/s10287-022-00422-2> to gross returns of financial assets. This is accomplished through an adaptive algorithm that identifies sub-groups of gross returns in each iteration by approximating their distribution with a sequence of two-component log-normal mixtures. These sub-groups emerge when a significant change in the distribution occurs below the median of the financial returns, with their boundary termed as the â change point" of the mixture. The process concludes when no further change points are detected. The outcome encompasses parameters of the leftmost mixture distributions and change points of the analyzed financial time series. The functionalities of the INFOSET package include: (i) modelling asset distribution detecting the parameters which describe left tail behaviour (infoset function), (ii) clustering, (iii) labeling of the financial series for predictive and classification purposes through a Left Risk measure based on the first change point (LR_cp function) (iv) portfolio construction (ptf_construction function). The package also provide a specific function to construct rolling windows of different length size and overlapping time.

This package provides a key-value store data structure. The keys are integers and the values can be any R object. This is like a list but indexed by a set of integers, not necessarily contiguous and possibly negative. The implementation uses a R6 class. These containers are not faster than lists but their usage can be more convenient for certain situations.

Reports and plots the conditional effect of each variable involved in a multiplicative interaction across the range of its moderators, together with the region over which that effect is distinguishable from zero. Extends the classic framework of Johnson and Neyman (1936) and Johnson and Fay (1950) <doi:10.1007/BF02288864> to three-way interactions and to Bayesian models. The single entry point JN() dispatches on the fitted object, with methods for lm()/glm() models, lme4 models, RSiena and multiSiena results, and matrices of posterior draws; support for further model classes is added by writing one jn_input() method. Results are classed objects with print(), summary() and plot() methods, and the figures carry data-density panels showing how much empirical support each part of the moderator range has. A detailed introduction can be found in Krause (2026) <doi:10.48550/arXiv.2604.22051>.

Launches a shiny based application for Nuclear Magnetic Resonance (NMR)data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of STOCSY method could be found at Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>.

Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009, <doi:10.1111/j.1467-9868.2009.00706.x>) and Oja, Sirkia and Eriksson's (AJS, 2006, <https://www.ajs.or.at/index.php/ajs/article/view/vol35,%20no2%263%20-%207>) method of two different scatter matrices to obtain an invariant coordinate system or independent components, depending on the underlying assumptions.