Perform survival simulation with parametric survival model generated from survreg function in survival package. In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to capture uncertainty in model parameters. Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.

This package provides historical datasets related to John Snow's 1854 cholera outbreak study in London. Includes data on cholera cases, water pump locations, and the street layout, enabling analysis and visualisation of the outbreak.

Estimate the internal consistency of your tasks with a permutation based split-half reliability approach. Unofficial release name: "I eat stickers all the time, dude!".

We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.

This package implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <arXiv:2212.08697>.

This package provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.

Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).

When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.

Implementation of the family of generalised age-period-cohort stochastic mortality models. This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.2307/2290201> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations.

Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.

Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) (Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285>) for supervised learning and Bayesian Causal Forests (BCF) (Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195>) for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers. Includes the grow-from-root algorithm for accelerated forest sampling (He and Hahn (2021) <doi:10.1080/01621459.2021.1942012>), a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) <doi:10.1080/01621459.2020.1813587>), and the cloglog BART model of Alam and Linero (2025) <doi:10.48550/arXiv.2502.00606> for ordinal outcomes.

Studies otolith shape variation among fish populations. Otoliths are calcified structures found in the inner ear of teleost fish and their shape has been known to vary among several fish populations and stocks, making them very useful in taxonomy, species identification and to study geographic variations. The package extends previously described software used for otolith shape analysis by allowing the user to automatically extract closed contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000) <doi:10.1111/1475-4983.00148>, choose from conducting either a Fourier or wavelet see Gençay et al (2001) <doi:10.1016/S0378-4371(00)00463-5> transform to the outlines and visualize the mean shape. The output of the package are independent Fourier or wavelet coefficients which can be directly imported into a wide range of statistical packages in R. The package might prove useful in studies of any two dimensional objects.

This package provides a graphical user interface (GUI) for fitting Bayesian regression models using the package brms which in turn relies on Stan (<https://mc-stan.org/>). The shinybrms GUI is a shiny app.

This package provides a comprehensive suite of portfolio spanning tests for asset pricing, such as Huberman and Kandel (1987) <doi:10.1111/j.1540-6261.1987.tb03917.x>, Gibbons et al. (1989) <doi:10.2307/1913625>, Kempf and Memmel (2006) <doi:10.1007/BF03396737>, Pesaran and Yamagata (2024) <doi:10.1093/jjfinec/nbad002>, and Gungor and Luger (2016) <doi:10.1080/07350015.2015.1019510>.

Package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.

This package provides a toolkit for stratified medicine, subgroup identification, and precision medicine. Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate models (counterfactual patient-level quantities, such as the conditional average treatment effect), (3) subgroup identification models (find subsets of patients with similar treatment effects), and (4) treatment effect estimation and inference (for the overall population and discovered subgroups). These tools can be customized and are directly used in PRISM (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019 <doi:10.48550/arXiv.1912.03337>).

We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The rjags package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015).

Generates synthetic clinical datasets that preserve statistical properties while reducing re-identification risk. Implements Gaussian copula simulation, bootstrap with noise injection, and Laplace noise perturbation, with built-in utility and privacy validation metrics. Useful for privacy-aware data sharing in multi-site clinical research. Validates synthetic data quality via distributional similarity (Kolmogorov-Smirnov), discriminative accuracy (real-vs-synthetic classifier), and nearest-neighbor privacy ratio. Methods described in Jordon et al. (2022) <doi:10.48550/arXiv.2205.03257> and Snoke et al. (2018) <doi:10.1111/rssa.12358>.

The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) <doi:10.1177/0008068320020518> and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674> in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.

This package provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.

Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.

This package implements Bayesian hierarchical models for estimating antibody kinetic parameters from longitudinal serological data. Fits two-phase within-host models capturing antibody rise, peak, and decay following pathogen infection, using JAGS for posterior inference. Designed as the upstream companion to the serocalculator package for end-to-end seroepidemiological analysis. Methods are described in Teunis and colleagues (2016) <doi:10.1016/j.epidem.2016.04.001> and Teunis and van Eijkeren (2020) <doi:10.1002/sim.8578>.

Calculation of solar zenith and azimuth angles.

This package provides methods for analysis of energy consumption data (electricity, gas, water) at different data measurement intervals. The package provides feature extraction methods and algorithms to prepare data for data mining and machine learning applications. Deatiled descriptions of the methods and their application can be found in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> "Enhancing energy efficiency in the residential sector with smart meter data analytics".