RStudio addin which provides a GUI to visualize and analyse networks. After finishing a session, the code to produce the plot is inserted in the current script. Alternatively, the function SNAhelperGadget() can be used directly from the console. Additional addins include the Netreader() for reading network files, Netbuilder() to create small networks via point and click, and the Componentlayouter() to layout networks with many components manually.

This package provides a minimalist implementation of model stacking by Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness.

This package provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects.

Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).

Empirical likelihood methods for asymptotically efficient estimation of models based on conditional or unconditional moment restrictions; see Kitamura, Tripathi & Ahn (2004) <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013) <doi:10.1002/cjs.11183>. Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in Rcpp for speed.

This package provides a reusable shiny module for viewing tabular data with a searchable reactable table and a variable summary sidebar built with bslib'.

Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. In case that the survival and truncation times are quasi-independent, the estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2018) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>. When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) <doi:10.1111/biom.13451>. Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert <liorr@clemson.edu> for questions regarding function coxDT and Yidan Shi <yidan.shi@pennmedicine.upenn.edu> for questions regarding function coxDTdep.

This package provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM).

This package provides a set of tools for estimating hierarchical linear models and effect sizes based on data from single-case designs. Functions are provided for calculating standardized mean difference effect sizes that are directly comparable to standardized mean differences estimated from between-subjects randomized experiments, as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>; and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) <DOI:10.1016/j.jsp.2023.02.002>. Includes an interactive web interface.

Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions.

Detection of anomalous space-time clusters using the scan statistics methodology. Focuses on prospective surveillance of data streams, scanning for clusters with ongoing anomalies. Hypothesis testing is made possible by Monte Carlo simulation. Allévius (2018) <doi:10.21105/joss.00515>.

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 provides a set of basic functions for creating Moodle XML output files suited for importing questions in Moodle (a learning management system, see <https://moodle.org/> for more information).

This package provides functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, <doi:10.1007/978-3-540-71918-2>), SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi:10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi:10.1080/00207543.2017.1380326>) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, <doi:10.13140/RG.2.2.35897.06242>).

Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators.

Automates the creation of Dockerfiles for deploying Shiny applications. By integrating with renv for dependency management and leveraging Docker-based solutions, it simplifies the process of containerizing Shiny apps, ensuring reproducibility and consistency across different environments. Additionally, it facilitates the setup of CI/CD pipelines for building Docker images on both GitLab and GitHub.

An interface to the Python package stanza <https://stanfordnlp.github.io/stanza/index.html>. stanza is a Python NLP library for many human languages. It contains support for running various accurate natural language processing tools on 60+ languages.

Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". . This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). . As an alternative, pre-built Docker containers such as the Rocker container r-devel-san or the multi-purpose container r-debug can be used. . This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. . The code samples are based on the examples from the Address Sanitizer Wiki at <https://github.com/google/sanitizers/wiki>.

The scrapeR package utilizes functions that fetch and extract text content from specified web pages. It handles HTTP errors and parses HTML efficiently. The package can handle hundreds of websites at a time using the scrapeR_in_batches() command.

Create sampling designs using the surface reconstruction algorithm. Original method by: Olsson, D. 2002. A method to optimize soil sampling from ancillary data. Poster presenterad at: NJF seminar no. 336, Implementation of Precision Farming in Practical Agriculture, 10-12 June 2002, Skara, Sweden.

This package implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Fan and Li (2001) <doi:10.1198/016214501753382273>, and Zhang (2010) <doi:10.1214/09-AOS729>.

This package provides functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled. For regression versions of the methods, Monte Carlo and asymptotic methods are used.

Spatial model calculation for static and dynamic panel data models, weights matrix creation and Bayesian model comparison. Bayesian model comparison methods were described by LeSage (2014) <doi:10.1016/j.spasta.2014.02.002>. The Lee'-'Yu transformation approach is described in Yu', De Jong and Lee (2008) <doi:10.1016/j.jeconom.2008.08.002>, Lee and Yu (2010) <doi:10.1016/j.jeconom.2009.08.001> and Lee and Yu (2010) <doi:10.1017/S0266466609100099>.

This package provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures.