This package provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

Equating of multiple forms using Item Response Theory (IRT) methods. See Battauz (2025) <doi:10.18637/jss.v115.i11> for a detailed description of the package. See Battauz M. (2017) <doi:10.1007/s11336-016-9517-x>, Battauz and Leoncio (2023) <doi:10.1177/01466216231151702> and Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>) for the methods to link multiple test forms.

This package provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.

Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap.

The cointegration based support vector regression model enables researchers to use data obtained from the cointegrating vector as input in the support vector regression model.

By using a multispectral image and ESRI shapefile (Point/ Line/ Polygon), a data table will be generated for classification, regression or other processing. The data table will be contained by band wise raster values and shapefile ids (User Defined).

This package implements likelihood-based evidence ratios for unified reporting in classical statistical testing. The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) <doi:10.5281/zenodo.18261076>.

In agricultural, post-harvest and processing, engineering and industrial experiments factors are often differentiated with ease with which they can change from experimental run to experimental run. This is due to the fact that one or more factors may be expensive or time consuming to change i.e. hard-to-change factors. These factors restrict the use of complete randomization as it may make the experiment expensive and time consuming. Split plot designs can be used for such situations. In general model estimation of split plot designs require the use of generalized least squares (GLS). However for some split-plot designs ordinary least squares (OLS) estimates are equivalent to generalized least squares (GLS) estimates. These types of designs are known in literature as equivalent-estimation split-plot design. For method details see, Macharia, H. and Goos, P.(2010) <doi:10.1080/00224065.2010.11917833>.Balanced split plot designs are designs which have an equal number of subplots within every whole plot. This package used to construct equivalent estimation balanced split plot designs for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function equivalent_BSPD().

Null models to analyse ecological networks (e.g. food webs, flower-visitation networks, seed-dispersal networks) and detect resource preferences or non-random interactions among network nodes. Tools are provided to run null models, test for and plot preferences, plot and analyse bipartite networks, and export null model results in a form compatible with other network analysis packages. The underlying null model was developed by Agusti et al. (2003) Molecular Ecology <doi:10.1046/j.1365-294X.2003.02014.x> and the full application to ecological networks by Vaughan et al. (2018) econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods in Ecology & Evolution, <doi:10.1111/2041-210X.12907>.

An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. Bayesian inference is done via MCMC sampling using Stan <https://mc-stan.org/>.

Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.

In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). The EDGAR database automated system collects all the different necessary filings and makes it publicly available. This package facilitates retrieving, storing, searching, and parsing of all the available filings on the EDGAR server. It downloads filings from SEC server in bulk with a single query. Additionally, it provides various useful functions: extracts 8-K triggering events, extract "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)" sections of annual statements, searches filings for desired keywords, provides sentiment measures, parses filing header information, and provides HTML view of SEC filings.

Second and backward-incompatible version of R package eodhd <https://eodhd.com/>, extended with a cache and quota system, also offering functions for cleaning and aggregating the financial data.

The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) <doi:10.48550/arXiv.2205.15111>.

An interface for performing climate matching using the Euclidean "Climatch" algorithm. Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the â Climatchâ algorithm, also referred to as the â Climateâ algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). â Climatch user manualâ . The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). <doi:10.1111/ddi.12391>.

This package provides a system for importing electrophysiological signal, based on the Waveform Database (WFDB) software package, written by Moody et al 2022 <doi:10.13026/gjvw-1m31>. A R-based system to utilize WFDB functions for reading and writing signal data, as well as functions for visualization and analysis are provided. A stable and broadly compatible class for working with signal data, supporting the reading in of cardiac electrophysiological files such as intracardiac electrograms, is introduced.

Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.

This package provides functions for computing critical values and implementing the one-sided/two-sided EL tests.

The elliptical factor model, as an extension of the traditional factor model, effectively overcomes the limitations of the traditional model when dealing with heavy-tailed characteristic data. This package implements sparse principal component methods (SPC) and bi-sparse online principal component estimation (SPOC) for parameter estimation. Includes functionality for calculating mean squared error, relative error, and loading matrix sparsity.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

By overloading the R help() function, this package allows users to use "docstring" style comments within their own defined functions. The package also provides additional functions to mimic the R basic example() function and the prototyping of packages.

Easily compute education inequality measures and the distribution of educational attainments for any group of countries, using the data set developed in Jorda, V. and Alonso, JM. (2017) <DOI:10.1016/j.worlddev.2016.10.005>. The package offers the possibility to compute not only the Gini index, but also generalized entropy measures for different values of the sensitivity parameter. In particular, the package includes functions to compute the mean log deviation, which is more sensitive to the bottom part of the distribution; the Theilâ s entropy measure, equally sensitive to all parts of the distribution; and finally, the GE measure when the sensitivity parameter is set equal to 2, which gives more weight to differences in higher education. The decomposition of these measures in the components between-country and within-country inequality is also provided. Two graphical tools are also provided, to analyse the evolution of the distribution of educational attainments: The cumulative distribution function and the Lorenz curve.

This package provides a convenient toolbox to import data exported from Electronic Data Capture (EDC) software TrialMaster'.

This package provides a simple, ticker-based interface for retrieving fundamental financial data from the United States Securities and Exchange Commission's EDGAR XBRL API <https://www.sec.gov/edgar/sec-api-documentation>. Functions return key financial ratios including earnings per share, return on equity, return on assets, debt-to-equity, current ratio, gross margin, operating margin, net margin, price-to-earnings, price-to-book, and dividend yield for any publicly traded U.S. company. Data is sourced directly from company 10-K annual filings, requiring no API key or paid subscription. Designed for use in quantitative finance courses and research workflows.

End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose.