Tests one hypothesis with several test statistics, correcting for multiple testing. The central function in the package is testtwice(). In a sensitivity analysis, the method has the largest design sensitivity of its component tests. The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.

Email Finder R Client Library. Search emails are based on the website You give one domain name and it returns all the email addresses found on the internet. Email Finder generates or retrieves the most likely email address from a domain name, a first name and a last name. Email verify checks the deliverability of a given email address, verifies if it has been found in our database, and returns their sources.

Allows users to analyze text and classify emotions such as happiness, sadness, anger, fear, and neutrality. It combines text preprocessing, TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction, and Random Forest classification to predict emotions and map them to corresponding emojis for enhanced sentiment visualization.

Develop, evaluate, and score multiple choice examinations, psychological scales, questionnaires, and similar types of data involving sequences of choices among one or more sets of answers. This version of the package should be considered as brand new. Almost all of the functions have been changed, including their argument list. See the file NEWS.Rd in the Inst folder for more information. Using the package does not require any formal statistical knowledge beyond what would be provided by a first course in statistics in a social science department. There the user would encounter the concept of probability and how it is used to model data and make decisions, and would become familiar with basic mathematical and statistical notation. Most of the output is in graphical form.

This package implements tic-tac-toe game to play on console, either with human or AI players. Various levels of AI players are trained through the Q-learning algorithm.

Estimation, selection and comparison of several families of transformations. The families of transformations included in the package are the following: Bickel-Doksum (Bickel and Doksum 1981 <doi:10.2307/2287831>), Box-Cox, Dual (Yang 2006 <doi:10.1016/j.econlet.2006.01.011>), Glog (Durbin et al. 2002 <doi:10.1093/bioinformatics/18.suppl_1.S105>), gpower (Kelmansky et al. 2013 <doi:10.1515/sagmb-2012-0030>), Log, Log-shift opt (Feng et al. 2016 <doi:10.1002/sta4.104>), Manly, modulus (John and Draper 1980 <doi:10.2307/2986305>), Neglog (Whittaker et al. 2005 <doi:10.1111/j.1467-9876.2005.00520.x>), Reciprocal and Yeo-Johnson. The package simplifies to compare linear models with untransformed and transformed dependent variable as well as linear models where the dependent variable is transformed with different transformations. Furthermore, the package employs maximum likelihood approaches, moments optimization and divergence minimization to estimate the optimal transformation parameter.

Efficient method for fitting nonparametric matrix trace regression model. The detailed description can be found in C. Lee, L. Li, H. Zhang, and M. Wang (2021). Nonparametric Trace Regression via Sign Series Representation. <arXiv:2105.01783>. The method employs the aggregation of structured sign series for trace regression (ASSIST) algorithm.

An implementation of the Thornley transport resistance plant growth model. The package can be used to simulate plant growth as forced by climate system variables. The package provides methods for formatting forcing variables, simulating growth dynamics and calibrating model parameters. For more information see Higgins et al. (2025) TTR.PGM: An R package for modelling the distributions and dynamics of plants using the Thornley transport resistance plant growth model. Methods in Ecology and Evolution. in press.

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>.

C source code and R wrappers for the tth/ttm TeX-to-HTML/MathML translators.

Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References: Nayak et al. (2024) <doi:10.1007/s40808-023-01944-7> and Nayak et al. (2024) <doi:10.1016/j.simpa.2024.100716>.

Testing, Implementation, and Forecasting of the THETA-SVM hybrid model. The THETA-SVM hybrid model combines the distinct strengths of the THETA model and the Support Vector Machine (SVM) model for time series forecasting.For method details see Bhattacharyya et al. (2022) <doi:10.1007/s11071-021-07099-3>.

These functions generate data frames on troop deployments and military basing using U.S. Department of Defense data on overseas military deployments. This package provides functions for pulling country-year troop deployment and basing data. Subsequent versions will hopefully include cross-national data on deploying countries.

This package provides a suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) <doi:10.1098/rsta.2011.0350>.

This package provides tools for decomposing differences in rate metrics between two groups into contributions from individual subgroups and visualizing them as a "Theseus Plot". Inspired by the story of the Ship of Theseus, the method replaces subgroup data from one group with that of another step by step, recalculating the overall metric at each stage to quantify subgroup contributions. A Theseus Plot combines the stepwise progression of a waterfall plot with the comparative bars of a bar chart, offering an intuitive way to understand subgroup-level effects.

It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 <doi:10.1080/03610926.2019.1570266>). Such methods, developed within a Bayesian framework through Stan <https://mc-stan.org/>, come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 <doi:10.18637/jss.v108.i01>).

Create structured, formatted HTML tables of in a flexible and convenient way.

Type hints are special comments within a function body indicating the intended nature of the function's arguments in terms of data types, dimensions and permitted values. The actual parameters with which the function is called are evaluated against these type hint comments at run-time.

This package creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.

This package provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the DoseFinding package, which is used for the fitting of the models. See <doi:10.1080/01621459.2017.1281813> for details.

Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.

This package provides methods and tools for generating forecasts at different temporal frequencies using a hierarchical time series approach.

Turn complex JSON data into tidy data frames.

This package provides a framework to download, parse, and store text datasets on the disk and load them when needed. Includes various sentiment lexicons and labeled text data sets for classification and analysis.