Stochastic dominance tests help ranking different distributions. The package implements the consistent test for stochastic dominance by Barrett and Donald (2003) <doi:10.1111/1468-0262.00390>. Specifically, it implements Barrett and Donald's Kolmogorov-Smirnov type tests for first- and second-order stochastic dominance based on bootstrapping 2 and 1.

Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under <https://statistikat.github.io/surveysd/articles/methodology.html>.

Sample size estimation and blinded sample size reestimation in Adaptive Study Design.

This package provides simple and powerful interfaces that facilitate interaction with ODBC data sources. Each data source gets its own unique and dedicated interface, wrapped around RODBC'. Communication settings are remembered between queries, and are managed silently in the background. The interfaces support multi-statement SQL scripts, which can be parameterised via metaprogramming structures and embedded R expressions.

This package provides functions are provided for the density function, distribution function, quantiles and random number generation for the skew hyperbolic t-distribution. There are also functions that fit the distribution to data. There are functions for the mean, variance, skewness, kurtosis and mode of a given distribution and to calculate moments of any order about any centre. To assess goodness of fit, there are functions to generate a Q-Q plot, a P-P plot and a tail plot.

Two-step and maximum likelihood estimation of Heckman-type sample selection models: standard sample selection models (Tobit-2), endogenous switching regression models (Tobit-5), sample selection models with binary dependent outcome variable, interval regression with sample selection (only ML estimation), and endogenous treatment effects models. These methods are described in the three vignettes that are included in this package and in econometric textbooks such as Greene (2011, Econometric Analysis, 7th edition, Pearson).

This package implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).

The skew logistic distribution is a quantile-defined generalisation of the logistic distribution (van Staden and King 2015). Provides random numbers, quantiles, probabilities, densities and density quantiles for the distribution. It provides Quantile-Quantile plots and method of L-Moments estimation (including asymptotic standard errors) for the distribution.

This package performs repeated nested cross-validation for Cox Proportionate Hazards, Cox Lasso, Survival Random Forest, and their ensemble. Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" <doi:10.1007/978-3-031-08337-2_15>, where the method is described as Ensemble 1.

Allows objects to be stored on disc and automatically recalled into memory, as required, by delayed assignment.

Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.

This package provides a computational framework for identification of B cell clones from Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main functions are included (identicalClones, hierarchicalClones, and spectralClones) that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) which share the same V gene, J gene and junction length. Nouri N and Kleinstein SH (2018) <doi: 10.1093/bioinformatics/bty235>. Nouri N and Kleinstein SH (2019) <doi: 10.1101/788620>. Gupta NT, et al. (2017) <doi: 10.4049/jimmunol.1601850>.

This package provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

Snow profiles describe the vertical (1D) stratigraphy of layered snow with different layer characteristics, such as grain type, hardness, deposition date, and many more. Hence, they represent a data format similar to multivariate time series containing categorical, ordinal, and numerical data types. Use this package to align snow profiles by matching their individual layers based on Dynamic Time Warping (DTW). The aligned profiles can then be assessed with an independent, global similarity measure that is geared towards avalanche hazard assessment. Finally, through exploiting data aggregation and clustering methods, the similarity measure provides the foundation for grouping and summarizing snow profiles according to similar hazard conditions. In particular, this package allows for averaging large numbers of snow profiles with DTW Barycenter Averaging and thereby facilitates the computation of individual layer distributions and summary statistics that are relevant for avalanche forecasting purposes. For more background information refer to Herla, Horton, Mair, and Haegeli (2021) <doi:10.5194/gmd-14-239-2021>, Herla, Mair, and Haegeli (2022) <doi:10.5194/tc-16-3149-2022>, and Horton, Herla, and Haegeli (2024) <doi:10.5194/egusphere-2024-1609>.

Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported.

This package implements estimation methods for shrinkage covariance matrices using user-specified covariance targets. The covariance target is a structured matrix towards which the unbiased sample covariance is shrunk, optionally incorporating prior knowledge. Shrinkage intensity is computed analytically. The method is described and applied to microarray gene expression data in Jelizarow et al. (2010) <doi:10.1093/bioinformatics/btq323>.

Regression inference for multiple populations by integrating summary-level data using stacked imputations. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2021) A synthetic data integration framework to leverage external summary-level information from heterogeneous populations <arXiv:2106.06835>.

This package provides a suite of helper functions to support Bayesian Kernel Machine Regression (BKMR) analyses in environmental health research. It enables the simulation of realistic multivariate exposure data using Multivariate Skewed Gamma distributions, estimation of distributional parameters by subgroup, and application of adaptive, data-driven thresholds for feature selection via Posterior Inclusion Probabilities (PIPs). It is especially suited for handling skewed exposure data and enhancing the interpretability of BKMR results through principled variable selection. The methodology is shown in Hasan et. al. (2025) <doi:10.1101/2025.04.14.25325822>.

This package provides useful UI components and input widgets for Shiny applications. The offered components allow to apply non-standard operations and view to your Shiny application, but also help to overcome common performance issues.

This package provides methods for statistical disclosure control in tabular data such as primary and secondary cell suppression as described for example in Hundepol et al. (2012) <doi:10.1002/9781118348239> are covered in this package.

An interactive shiny application to assist in determining sample sizes for common survey designs such as simple random sampling', stratified sampling', and cluster sampling'. It includes formulas, helper calculators, and illustrative examples.

Data Sets for Kumar and Petersen (2012). Statistical Methods in Customer Relationship Management, Wiley: New York.

Calculates and plots the SiZer map for scatterplot data. A SiZer map is a way of examining when the p-th derivative of a scatterplot-smoother is significantly negative, possibly zero or significantly positive across a range of smoothing bandwidths.

This package implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains.