This package provides an interface to a large number of classification and regression techniques. These techniques include machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Also included:

• Generic resampling, including cross-validation, bootstrapping and subsampling;

• Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems;

• Filter and wrapper methods for feature selection;

• Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling.

Most operations can be parallelized.

Flexible and informed regression with Multiple Change Points. mcp can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. mcp supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. mcp is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.

This package provides a number of functions to facilitate extracting information in YAML fragments from one or multiple files, optionally structuring the information in a data.tree'. YAML (recursive acronym for "YAML ain't Markup Language") is a convention for specifying structured data in a format that is both machine- and human-readable. YAML therefore lends itself well for embedding (meta)data in plain text files, such as Markdown files. This principle is implemented in yum with minimal dependencies (i.e. only the yaml packages, and the data.tree package can be used to enable additional functionality).

This package provides tools to easily access and analyze Canadian Election Study data. The package simplifies the process of downloading, cleaning, and using CES datasets for political science research and analysis. The Canadian Election Study ('CES') has been conducted during federal elections since 1965, surveying Canadians on their political preferences, engagement, and demographics. Data is accessed from the Borealis Data repository <https://borealisdata.ca/>, which serves as the official host for CES datasets. This package is not officially affiliated with the Canadian Election Study or Borealis Data, and users should cite the original data sources in their work.

Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.

This package provides tools to fit Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, and a simulation module for various binary data matrices.

Based on the aggregated shares retained by individual firms or actors within a market or space, the Herfindahl-Hirschman Index (HHI) measures the level of concentration in a space. This package allows for intuitive and straightforward computation of HHI scores, requiring placement of objects of interest directly into the function. The package also includes a plot function for quick visual display of an HHI time series using any measure of time (year, quarter, month, etc.). For usage, please cite the Journal of Open Source Software paper associated with the package: Waggoner, Philip D. (2018) <doi:10.21105/joss.00828>.

This package provides a novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity.

This package provides functions to identify plausible and replicable factor structures for a set of variables via k-fold cross validation. The process combines the exploratory and confirmatory factor analytic approach to scale development (Flora & Flake, 2017) <doi:10.1037/cbs0000069> with a cross validation technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009) <isbn:978-0-387-21606-5>. Also available are functions to determine k by drawing on power analytic techniques for covariance structures (MacCallum, Browne, & Sugawara, 1996) <doi:10.1037/1082-989X.1.2.130>, generate model syntax, and summarize results in a report.

The Structural Topic and Sentiment-Discourse (STS) model allows researchers to estimate topic models with document-level metadata that determines both topic prevalence and sentiment-discourse. The sentiment-discourse is modeled as a document-level latent variable for each topic that modulates the word frequency within a topic. These latent topic sentiment-discourse variables are controlled by the document-level metadata. The STS model can be useful for regression analysis with text data in addition to topic modelingâ s traditional use of descriptive analysis. The method was developed in Chen and Mankad (2024) <doi:10.1287/mnsc.2022.00261>.

This is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).

This package provides a collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica bSi in inland and marine sediments. These tools share common data representations and follow a consistent API design. The primary goal of the bSi package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire bSi analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems.

This package produces statistical indicators of the impact of migration on the socio-demographic composition of an area. Three measures can be used: ratios, percentages and the Duncan index of dissimilarity. The input data files are assumed to be in an origin-destination matrix format, with each cell representing a flow count between an origin and a destination area. Columns are expected to represent origins, and rows are expected to represent destinations. The first row and column are assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018) <doi:10.1080/00324728.2017.1416155> for technical details.

The aim of the package is two-fold: (i) To implement the MMD method for attribution of individuals to sources using the Hamming distance between multilocus genotypes. (ii) To select informative genetic markers based on information theory concepts (entropy, mutual information and redundancy). The package implements the functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to efficiently attribute individuals to source populations. Scientific Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>. See more details and examples in the README file.

This package provides methods for decomposing seasonal data: STR (a Seasonal-Trend time series decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can also be used for forecasting.

An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used to build rules, and glmnet is used to fit a sparse linear model on the raw and rule features. The result is a model that learns similarly to a tree ensemble, while often offering improved interpretability and achieving improved scoring runtime in live applications. Several algorithms for reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to several million rows and support sparse representations to handle tens of thousands of dimensions.

This package implements Meng's data defect index (ddi), which represents the degree of sample bias relative to an iid sample. The data defect correlation (ddc) represents the correlation between the outcome of interest and the selection into the sample; when the sample selection is independent across the population, the ddc is zero. Details are in Meng (2018) <doi:10.1214/18-AOAS1161SF>, "Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election." Survey estimates from the Cooperative Congressional Election Study (CCES) is included to replicate the article's results.

The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes.

This package provides a collection of psychometric methods to process item metadata and use target assessment and measurement blueprint constraints to assemble a test form. Currently two automatic test assembly (ata) approaches are enabled. For example, the weighted (positive) deviations method, wdm(), proposed by Swanson and Stocking (1993) <doi:10.1177/014662169301700205> was implemented in its full specification allowing for both item selection as well as test form refinement. The linear constraint programming approach, atalp(), uses the linear equation solver by Berkelaar et. al (2014) <http://lpsolve.sourceforge.net/5.5/> to enable a variety of approaches to select items.

Computations for approximations and alternatives for the DPQ (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. -- For several distribution functions, provide functions implementing formulas from Johnson, Kotz, and Kemp (1992) <doi:10.1002/bimj.4710360207> and Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous distributions respectively. This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve standard R pbeta(), qgamma(), ..., etc: '"dpq"'-functions.

Opinionated functions that enable easier and faster analysis of Viva Insights data. There are three main types of functions in wpa': (i) Standard functions create a ggplot visual or a summary table based on a specific Viva Insights metric; (2) Report Generation functions generate HTML reports on a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover more specific applications (e.g. Subject Line text mining) of Viva Insights data. This package adheres to tidyverse principles and works well with the pipe syntax. wpa is built with the beginner-to-intermediate R users in mind, and is optimised for simplicity.

Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model (GLM). Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. This package provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data.

This package provides a distance density clustering (DDC) algorithm in R. DDC uses dynamic time warping (DTW) to compute a similarity matrix, based on which cluster centers and cluster assignments are found. DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) <doi:10.1109/ICDMW.2017.11>).

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.