Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.

Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports cross-sectional network models fitted using the packages BDgraph', bgms and BGGM', as well as network comparison tests fitted using the packages bgms and BBGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for BDgraph and bgms it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.

Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.

This package provides functions for calculating mortality indicators. These include geometric interpolation between two periods and projections for future years, as described in the textbook by Laurenti, Mello Jorge, Lebrão and Gotlieb (2005, ISBN:9788512408309), the standardised mortality ratio (Bruce, Pope and Stanistreet, 2018, ISBN:9781118665411), the age-adjusted mortality rate (direct standardisation), years of potential life lost (Gardner and Sanborn, 1990, <doi:10.1097/00001648-199007000-00012>; Ma, Ward, Siegel and Jemal, 2015, <doi:10.1001/jama.2015.12319>), and age-standardised years of potential life lost (Silva Filho et al., 2024 <doi:10.1590/1413-81232024293.04702023EN>). Confidence intervals for the standardised mortality ratio are obtained according to Vandenbroucke (1982) <doi:10.1093/oxfordjournals.aje.a113306> and Ulm (1990) <doi:10.1093/oxfordjournals.aje.a115507>. The function also includes a function that produces a graph similar to an age pyramid.

This package provides a unified interface for connecting to databases ('SQLite', MySQL', PostgreSQL'). Just provide the database name and the package will ask you questions to help you configure the connection and setup your credentials. Once database configuration and connection has been set up once, you won't have to do it ever again.

Correlation chart of two set (x and y) of data. Using Quantiles. Visualize the effect of factor.

Descriptive Statistics is essential for publishing articles. This package can perform descriptive statistics according to different data types. If the data is a continuous variable, the mean and standard deviation or median and quartiles are automatically output; if the data is a categorical variable, the number and percentage are automatically output. In addition, if you enter two variables in this package, the two variables will be described and their relationships will be tested automatically according to their data types. For example, if one of the two input variables is a categorical variable, another variable will be described hierarchically based on the categorical variable and the statistical differences between different groups will be compared using appropriate statistical methods. And for groups of more than two, the post hoc test will be applied. For more information on the methods we used, please see the following references: Libiseller, C. and Grimvall, A. (2002) <doi:10.1002/env.507>, Patefield, W. M. (1981) <doi:10.2307/2346669>, Hope, A. C. A. (1968) <doi:10.1111/J.2517-6161.1968.TB00759.X>, Mehta, C. R. and Patel, N. R. (1983) <doi:10.1080/01621459.1983.10477989>, Mehta, C. R. and Patel, N. R. (1986) <doi:10.1145/6497.214326>, Clarkson, D. B., Fan, Y. and Joe, H. (1993) <doi:10.1145/168173.168412>, Cochran, W. G. (1954) <doi:10.2307/3001616>, Armitage, P. (1955) <doi:10.2307/3001775>, Szabo, A. (2016) <doi:10.1080/00031305.2017.1407823>, David, F. B. (1972) <doi:10.1080/01621459.1972.10481279>, Joanes, D. N. and Gill, C. A. (1998) <doi:10.1111/1467-9884.00122>, Dunn, O. J. (1964) <doi:10.1080/00401706.1964.10490181>, Copenhaver, M. D. and Holland, B. S. (1988) <doi:10.1080/00949658808811082>, Chambers, J. M., Freeny, A. and Heiberger, R. M. (1992) <doi:10.1201/9780203738535-5>, Shaffer, J. P. (1995) <doi:10.1146/annurev.ps.46.020195.003021>, Myles, H. and Douglas, A. W. (1973) <doi:10.2307/2063815>, Rahman, M. and Tiwari, R. (2012) <doi:10.4236/health.2012.410139>, Thode, H. J. (2002) <doi:10.1201/9780203910894>, Jonckheere, A. R. (1954) <doi:10.2307/2333011>, Terpstra, T. J. (1952) <doi:10.1016/S1385-7258(52)50043-X>.

This package provides functions and data sets to perform and demonstrate community ecology statistical tests, including Hutcheson's t-test (Hutcheson (1970) <doi:10.1016/0022-5193(70)90124-4>, Zar (2010) ISBN:9780321656865).

This package provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.

Detect outliers in one-dimensional data.

Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at <doi:10.48550/arXiv.2409.18227>.

Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (fdiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. Also includes two-sample block-wise empirical likelihood (BEL) and a frequency-domain empirical likelihood test for autocorrelation differences (FDEL). Methods for EL, P-P, Q-Q, ROC, qdiff and fdiff are based on Valeinis and Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.

This package provides tools for modelling electric vehicle charging sessions into generic groups with similar connection patterns called "user profiles", using Gaussian Mixture Models clustering. The clustering and profiling methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) <doi:10.1016/j.ijepes.2021.107195>.

This package provides a dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects. Johnson WE & Li C (2007) <doi:10.1093/biostatistics/kxj037> "Adjusting batch effects in microarray expression data using empirical Bayes methods." Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnes M, Phillips ML, Trivedi MH, Weissman MM, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.11.024> "Harmonization of cortical thickness measurements across scanners and sites." Fortin J-P, Parker D, Tun<e7> B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.08.047> "Harmonization of multi-site diffusion tensor imaging data.".

This package provides a comprehensive collection of datasets related to education, covering topics such as student performance, learning methods, test scores, absenteeism, and other educational metrics. This package serves as a resource for educational researchers, data analysts, and statisticians to explore and analyze data in the field of education.

This package provides a collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.

This package provides a function to generate comment boxes framed with # characters, with configurable width and text alignment (left, center, or right). Useful for formatting scripts and improving code readability.

Illustrates the concepts developed in Sarkar and Rashid (2019, ISSN:0025-5742) <http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiH4deL3q3xAhWX73MBHR_wDaYQFnoECAUQAw&url=https%3A%2F%2Fwww.indianmathsociety.org.in%2Fmathstudent-part-2-2019.pdf&usg=AOvVaw3SY--3T6UAWUnH5-Nj6bSc>. This package helps a user guess four things (mean, MD, scaled MSD, and RMSD) before they get the SD. 1) The package displays the Empirical Cumulative Distribution Function (ECDF) of the given data. The user must choose the value of the mean by equating the areas of two colored (blue and green) regions. The package gives feedback to improve the choice until it is correct. Alternatively, the reader may continue with a different guess for the center (not necessarily the mean). 2) The user chooses the values of the Mean Deviation (MD) based on the ECDF of the deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 3) The user chooses the Scaled Mean Squared Deviation (MSD) based on the ECDF of the scaled square deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 4) The user chooses the Root Mean Squared Deviation (RMSD) by ensuring that its intersection with the ECDF of the deviations is at the same height as the intersection between the scaled MSD and the ECDF of the scaled squared deviations. Additionally, the intersection of two blue lines (the green dot) should fall on the vertical line at the maximum deviation. 5) Finally, if the mean is chosen correctly, only then the user can view the population SD (the same as the RMSD) and the sample SD (sqrt(n/(n-1))*RMSD) by clicking the respective buttons. If the mean is chosen incorrectly, the user is asked to correct it.

Evaluates the empirical characteristic function of univariate and multivariate samples. This package uses RcppArmadillo for fast evaluation. It is also possible to export the code to be used in other packages at C++ level.

The main function, ProtectTable(), performs table suppression according to a frequency rule with a data set as the only required input. Within this function, protectTable(), protect_linked_tables() or runArgusBatchFile() in package sdcTable is called. Lists of level-hierarchy (parameter dimList') and other required input to these functions are created automatically. The suppression method Gauss (default) is implemented independently of sdcTable'. The function, PTgui(), starts a graphical user interface based on the shiny package.

This package provides a rich toolkit of using the whole building simulation program EnergyPlus'(<https://energyplus.net>), which enables programmatic navigation, modification of EnergyPlus models and makes it less painful to do parametric simulations and analysis.

Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.

This is a data-only package containing model objects that predict human energy expenditure from wearable sensor data. Supported methods include the neural networks of Montoye et al. (2017) <doi:10.1080/1091367X.2017.1337638> and the models of Staudenmayer et al. (2015) <doi:10.1152/japplphysiol.00026.2015>, one a linear model and the other a random forest. The package is intended as a spoke for the hub-package accelEE', which brings together the above methods and others from packages such as Sojourn and TwoRegression.'.

Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.