Collection of tools for the analysis of the resilience of dynamic networks. Created as a classroom project.

This package provides an easy way to report the results of ROC analysis, including: 1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy), SEN (sensitivity), SPE (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), PPV (positive predictive value), NPV (negative predictive value), PPA (percentage of positive accordance), NPA (percentage of negative accordance), TPA (percentage of total accordance), KAPPA (kappa value).

Allows caching of raw data directly in R code. This allows R scripts and R Notebooks to be shared and re-run on a machine without access to the original data. Cached data is encoded into an ASCII string that can be pasted into R code. When the code is run, the data is automatically loaded from the cached version if the original data file is unavailable. Works best for small datasets (a few hundred observations).

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the RMarkdown vignette: "'refitME R-package tutorial".

Download, prepare and analyze data from large-scale assessments and surveys with complex sampling and assessment design (see Rutkowski', 2010 <doi:10.3102/0013189X10363170>). Such studies are, for example, international assessments like TIMSS', PIRLS and PISA'. A graphical interface is available for the non-technical user.The package includes functions to covert the original data from SPSS into R data sets keeping the user-defined missing values, merge data from different respondents and/or countries, generate variable dictionaries, modify data, produce descriptive statistics (percentages, means, percentiles, benchmarks) and multivariate statistics (correlations, linear regression, binary logistic regression). The number of supported studies and analysis types increases with every next release. For a general presentation of the package, see Mirazchiyski', 2021a (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of the package, see Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).

This package provides methods for downloading and processing data and metadata from Kolada', the official Swedish regions and municipalities database <https://www.kolada.se/>.

R Interface to JDemetra+ 3.x (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to decompose a time series, including high-frequency data with multiple periodicities.

Reallocating the respective lessons by hours (respecting the constraints induced by the existence of coupled lessons) so that the total number of gaps is as small as possible.

Datasets and utilities for teaching linear models in the context of the BetaBit universe (StatPunk). The package provides simulated datasets based on the fictional LifeCalc algorithmic scoring system, illustrating concepts such as simple regression, ANOVA, ANCOVA, hierarchical models, multicollinearity, model selection (AIC, BIC), and regularisation (LASSO, Ridge). Each dataset is accompanied by a narrative context connecting statistical methodology to questions of algorithmic fairness and social consequence.

This package provides an interface between R and PostGIS'-enabled PostgreSQL databases to transparently transfer spatial data. Both vector (points, lines, polygons) and raster data are supported in read and write modes. Also provides convenience functions to execute common procedures in PostgreSQL/PostGIS'.

Recursive display of names and paths of all the items nested within sublists of a list object.

Generate, simulate and visualise ODE models of consumer-resource interactions. At its core, rescomp provides a resource competition modelling focused interface to deSolve', alongside flexible functions for visualising model properties and dynamics. More information, documentation and examples can be found on the package website.

Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. It includes models for real, categorical, counting, functional and ranking data. This package contains the minimal R interface of the C++ MixtComp library.

This package provides typed parameter documentation tags for integration with roxygen2'. Typed parameter tags provide a consistent interface for annotating expected types for parameters and returned values. Tools for converting from existing styles are also provided to easily adapt projects which implement typed documentation by convention rather than tag. Use the default format or provide your own.

This package implements the empirical Bayes ranking method described in Henderson and Hartman (2025) <doi:10.48550/arXiv.2511.16530>. The data structure of interest is a collection of cluster-specific effect size estimates, associated standard errors for these estimates, and cluster-level covariates. Estimates of the rankings of the cluster-specific effect sizes after adjusting for cluster-level covariates are generated.

Analyze multi-level one-way experimental designs where there are unequal sample sizes and population variance homogeneity can not be assumed. To conduct the Gabriel test <doi:10.2307/2286265>, create two vectors: one for your observations and one for the factor level of each observation. The function, rgabriel, conduct the test and save the output as a vector to input into the gabriel.plot function, which produces a confidence interval plot for Multiple Comparison.

This package provides functions to assist in performing probabilistic record linkage and deduplication: generating pairs, comparing records, em-algorithm for estimating m- and u-probabilities (I. Fellegi & A. Sunter (1969) <doi:10.1080/01621459.1969.10501049>, T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0), forcing one-to-one matching. Can also be used for pre- and post-processing for machine learning methods for record linkage. Focus is on memory, CPU performance and flexibility.

This package provides functions to perform robust stepwise split regularized regression. The approach first uses a robust stepwise algorithm to split the variables into the models of an ensemble. An adaptive robust regularized estimator is then applied to each subset of predictors in the models of an ensemble.

Interactive data tables for R, based on the React Table JavaScript library. Provides an HTML widget that can be used in R Markdown or Quarto documents, Shiny applications, or viewed from an R console.

Estimation of abundance and other demographic parameters for closed populations, open populations and the robust design in capture-recapture experiments using loglinear models.

This package provides an interface with the Wildbook mark-recapture ecological database framework. It helps users to pull data from the Wildbook framework and format data for further analysis with mark-recapture applications like Program MARK (which can be accessed via the RMark package in R'). Further information on the Wildbook framework is available at: <http://www.wildbook.org/doku.php>.

Interface for the Google Ads API'. Google Ads is an online advertising service that enables advertisers to display advertising to web users (see <https://developers.google.com/google-ads/> for more information).

This package provides methods for randomization inference in group-randomized trials. Specifically, it can be used to analyze the treatment effect of stratified data with multiple clusters in each stratum with treatment given on cluster level. User may also input as many covariates as they want to fit the data. Methods are described by Dylan S Small et al., (2012) <doi:10.1198/016214507000000897>.

This package provides a memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. rMVP contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, R implementation from GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).