An integrated set of extensions to the ergm package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). tergm is a part of the statnet suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.

Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.

Instance feature calculation and evolutionary instance generation for the traveling salesman problem. Also contains code to "morph" two TSP instances into each other. And the possibility to conveniently run a couple of solvers on TSP instances.

Draws tornado plots for model sensitivity to univariate changes. Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package. Also draws variable importance plots.

Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank correlations. Test statistics as well as one sided or two sided p-values are provided for all correlations. Multiple correlations and p-values can be calculated simultaneously across multiple variables. In addition, users will have the option to use the partial, semipartial, and generalized partial correlations; where the partial and semipartial correlations use linear, logistic, or Poisson regression to modify the specified variable.

Measuring angles between points in a landscape is much easier than measuring distances. When the location of three points is known the position of the observer can be determined based solely on the angles between these points as seen by the observer. This task (known as triangulation) however requires onerous calculations - these calculations are automated by this package.

Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.

This package provides tools that stem and lemmatize text. Stemming is a process that removes endings such as affixes. Lemmatization is the process of grouping inflected forms together as a single base form.

Implementation of unconditional Bernoulli Scan Statistic developed by Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> for hierarchical tree structures. Tree-based Scan Statistics are an exploratory method to identify event clusters across the space of a hierarchical tree.

To handle higher-order tensor data. See Kolda and Bader (2009) <doi:10.1137/07070111X> for details on tensor. While existing packages on tensor data extend the base array class to some data classes, this package serves as an alternative resort to handle tensor only as array class. Some functionalities related to missingness are also supported.

The two-parameter Xgamma and Poisson Xgamma distributions are analyzed, covering standard distribution and regression functions, maximum likelihood estimation, quantile functions, probability density and mass functions, cumulative distribution functions, and random number generation. References include: "Sen, S., Chandra, N. and Maiti, S. S. (2018). On properties and applications of a two-parameter XGamma distribution. Journal of Statistical Theory and Applications, 17(4): 674--685. <doi:10.2991/jsta.2018.17.4.9>." "Wani, M. A., Ahmad, P. B., Para, B. A. and Elah, N. (2023). A new regression model for count data with applications to health care data. International Journal of Data Science and Analytics. <doi:10.1007/s41060-023-00453-1>.".

Here we provide tools for the computation and factorization of high-dimensional tensor products that are formed by smaller matrices. The methods are based on properties of Kronecker products (Searle 1982, p. 265, ISBN-10: 0470009616). We evaluated this methodology by benchmark testing and illustrated its use in Gaussian Linear Models ('Lopez-Cruz et al., 2024') <doi:10.1093/g3journal/jkae001>.

For when your colors absolutely should not be excluded from the narrative.

The maximum likelihood classifier (MLC) is one of the most common classifiers used for remote sensing imagery. This package uses RcppArmadillo to provide a fast implementation of the MLC to train and predict over tabular data (data.frame). The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.

In order to easily integrate geoRSS data into analysis, tidygeoRSS parses geo feeds and returns tidy simple features data frames.

This package provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis. This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>. Functions for log-ratio principal component analysis with condition number computations and log-ratio discriminant analysis have been added to the package.

Facilitates development and application of two-regression algorithms for research-grade wearable devices. It provides an easy way for users to access previously-developed algorithms, and also to develop their own. Initial motivation came from Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2018) <doi:10.1249/MSS.0000000000001532>. However, other algorithms are now supported. Please see the associated references in the package documentation for full details of the algorithms that are supported.

Feature selection algorithm that extracts features in highly correlated spaces. The extracted features are meant to be fed into simple explainable models such as linear or logistic regressions. The package is useful in the field of explainable modelling as a way to understand variable behavior.

The German national forest inventory uses angle count sampling, a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung. Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 vom Juni 1947` and extended by Grosenbaugh (<https://academic.oup.com/jof/article-abstract/50/1/32/4684174>) as probability proportional to size sampling. When plots are located near stand boundaries, their sizes and hence their probabilities need to be corrected.

Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. TDApplied provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.

This package contains R functions for simulating and estimating integer-valued trawl processes as described in the article Veraart (2019),"Modeling, simulation and inference for multivariate time series of counts using trawl processes", Journal of Multivariate Analysis, 169, pages 110-129, <doi:10.1016/j.jmva.2018.08.012> and for simulating random vectors from the bivariate negative binomial and the bi- and trivariate logarithmic series distributions.

Extract trends from monthly and quarterly economic time series. Provides two main functions: augment_trends() for pipe-friendly tibble workflows and extract_trends() for direct time series analysis. Includes key econometric filters and modern parameter experimentation tools.

This package provides functions to get personal Google Scholar profile data from web API and show it in table or figure format.

This package provides functions to access historical and real-time national hydrometric data from Water Survey of Canada data sources and then applies tidy data principles.