This package provides a set of functions used in teaching STATS 201/208 Data Analysis at the University of Auckland. The functions are designed to make parts of R more accessible to a large undergraduate population who are mostly not statistics majors.

This package implements an extension of the Generalized Berk-Jones (GBJ) statistic for survival data, sGBJ. It computes the sGBJ statistic and its p-value for testing the association between a gene set and a time-to-event outcome with possible adjustment on additional covariates. Detailed method is available at Villain L, Ferte T, Thiebaut R and Hejblum BP (2021) <doi:10.1101/2021.09.07.459329>.

Estimates a covariance matrix using Stein's isotonized covariance estimator, or a related estimator suggested by Haff.

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.

Collection of model estimation, and model plotting functions related to the STEPCAM family of community assembly models. STEPCAM is a STEPwise Community Assembly Model that infers the relative contribution of Dispersal Assembly, Habitat Filtering and Limiting Similarity from a dataset consisting of the combination of trait and abundance data. See also <doi:10.1890/14-0454.1> for more information.

Phenotypic analysis of data coming from high throughput phenotyping (HTP) platforms, including different types of outlier detection, spatial analysis, and parameter estimation. The package is being developed within the EPPN2020 project (<https://cordis.europa.eu/project/id/731013>). Some functions have been created to be used in conjunction with the R package asreml for the ASReml software, which can be obtained upon purchase from VSN international (<https://vsni.co.uk/software/asreml-r/>).

An extension to the individual claim simulator called SynthETIC (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" <arXiv:2109.04058>.

Fast single trait Genome Wide Association Studies (GWAS) following the method described in Kang et al. (2010), <doi:10.1038/ng.548>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Sensitivity to unmeasured biases in an observational study that is a full match. Function senfm() performs tests and function senfmCI() creates confidence intervals. The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.

Generate the same random numbers in R and Python.

Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting, bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.

This app enables interactive validation, interpretation and visualization of structural topic models from the stm package by Roberts and others (2014) <doi:10.1111/ajps.12103>. It also includes helper functions for model diagnostics and extracting data from effect estimates.

Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.

This package provides a set of functions to build a scoring model from beginning to end, leading the user to follow an efficient and organized development process, reducing significantly the time spent on data exploration, variable selection, feature engineering, binning and model selection among other recurrent tasks. The package also incorporates monotonic and customized binning, scaling capabilities that transforms logistic coefficients into points for a better business understanding and calculates and visualizes classic performance metrics of a classification model.

Animal movement models including Moving-Resting Process with Embedded Brownian Motion (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>; Pozdnyakov et al., 2017, <doi:10.1007/s11009-017-9547-6>), Brownian Motion with Measurement Error (Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>), Moving-Resting-Handling Process with Embedded Brownian Motion (Pozdnyakov et al., 2020, <doi:10.1007/s11009-020-09774-1>), Moving-Resting Process with Measurement Error (Hu et al., 2021, <doi:10.1111/2041-210X.13694>), Moving-Moving Process with two Embedded Brownian Motions.

Performance evaluation metrics for supervised and unsupervised machine learning, statistical learning and artificial intelligence applications. Core computations are implemented in C++ for scalability and efficiency.

Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. Comprehensive documentation and vignettes guide users through implementation and interpretation of results.

Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).

Work with containers over the Docker API. Rather than using system calls to interact with a docker client, using the API directly means that we can receive richer information from docker. The interface in the package is automatically generated using the OpenAPI (a.k.a., swagger') specification, and all return values are checked in order to make them type stable.

This package provides tools for making, retrieving, displaying and solving sudoku games. This package is an alternative to the earlier sudoku-solver package, sudoku'. The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints.

This package performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.

Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions.

When working across multiple machines and, similarly for reproducible research, it can be time consuming to ensure that you have all of the needed packages installed and loaded and that the correct working directory is set. simpleSetup provides simple functions for making these tasks more straightforward.

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.