This package provides an implementation of the maximum likelihood methods for deriving Elo scores as published in Foerster, Franz et al. (2016) <DOI:10.1038/srep35404>.

Software of esDesign is developed to implement the adaptive enrichment designs with sample size re-estimation presented in Lin et al. (2021) <doi: 10.1016/j.cct.2020.106216>. In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.

This package provides a small group of functions to read in a data dictionary and the corresponding data table from Excel and to automate the cleaning, re-coding and creation of simple calculated variables. This package was designed to be a companion to the macro-enabled Excel template available on the GitHub site, but works with any similarly-formatted Excel data.

This package provides a simple approach to using a probit or logit analysis to calculate lethal concentration (LC) or time (LT) and the appropriate fiducial confidence limits desired for selected LC or LT for ecotoxicology studies (Finney 1971; Wheeler et al. 2006; Robertson et al. 2007). The simplicity of ecotox comes from the syntax it implies within its functions which are similar to functions like glm() and lm(). In addition to the simplicity of the syntax, a comprehensive data frame is produced which gives the user a predicted LC or LT value for the desired level and a suite of important parameters such as fiducial confidence limits and slope. Finney, D.J. (1971, ISBN: 052108041X); Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) <doi:10.1897/05-320R.1>; Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K. (2007, ISBN: 0849323312).

Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages BDgraph', bgms and BGGM', as well as network comparison fitted using the 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.

Easily import multi-frequency acoustic data stored in HAC files (see <doi:10.17895/ices.pub.5482> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.

Analysis of elliptical tubes with applications in biological modeling. The package is based on the references: Taheri, M., Pizer, S. M., & Schulz, J. (2024) "The Mean Shape under the Relative Curvature Condition." Journal of Computational and Graphical Statistics <doi:10.1080/10618600.2025.2535600> and arXiv <doi:10.48550/arXiv.2404.01043>. Mohsen Taheri Shalmani (2024) "Shape Statistics via Skeletal Structures", PhD Thesis, University of Stavanger, Norway <doi:10.13140/RG.2.2.34500.23685>. Key features include constructing discrete elliptical tubes, calculating transformations, validating structures under the Relative Curvature Condition (RCC), computing means, and generating simulations. Supports intrinsic and non-intrinsic mean calculations and transformations, size estimation, plotting, and random sample generation based on a reference tube. The intrinsic approach relies on the interior path of the original non-convex space, incorporating the RCC, while the non-intrinsic approach uses a basic robotic arm transformation that disregards the RCC.

Pacote para análise de delineamentos experimentais (DIC, DBC e DQL), experimentos em esquema fatorial duplo (em DIC e DBC), experimentos em parcelas subdivididas (em DIC e DBC), experimentos em esquema fatorial duplo com um tratamento adicional (em DIC e DBC), experimentos em fatorial triplo (em DIC e DBC) e experimentos em esquema fatorial triplo com um tratamento adicional (em DIC e DBC), fazendo analise de variancia e comparacao de multiplas medias (para tratamentos qualitativos), ou ajustando modelos de regressao ate a terceira potencia (para tratamentos quantitativos); analise de residuos (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.

This package provides functions for estimating catalytic constant and Michaelis-Menten constant for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs sampler based on the Bayesian framework.

Ensemble Model Output Statistics to create probabilistic forecasts from ensemble forecasts and weather observations.

Datasets from Nelson, Coffin and Copeland "Introductory Statistics for Engineering Experimentation" (Elsevier, 2003) with sample code.

Perform tensor operations using a concise yet expressive syntax inspired by the Python library of the same name. Reshape, rearrange, and combine multidimensional arrays for scientific computing, machine learning, and data analysis. Einops simplifies complex manipulations, making code more maintainable and intuitive. The original implementation is demonstrated in Rogozhnikov (2022) <https://openreview.net/forum?id=oapKSVM2bcj>.

It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.

An implementation for using efficient initials to compute the maximal eigenpair in R. It provides three algorithms to find the efficient initials under two cases: the tridiagonal matrix case and the general matrix case. Besides, it also provides two algorithms for the next to the maximal eigenpair under these two cases.

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 a Shiny'-based toolkit for item/test analysis. It is designed for multiple-choice, true-false, and open-ended questions. The toolkit is usable with datasets in 1-0 or other formats. Key analyses include difficulty, discrimination, response-option analysis, reports. The classical test theory methods used are described in Ebel & Frisbie (1991, ISBN:978-0132892314).

This package provides functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).

An implementation of the clustering methods of categorical data discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical data via ensembling dissimilarity matrices. Preprint <arXiv:1506.07930>.

Errors in data can be located and removed using validation rules from package validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, chapter 7.

Support functions for R-based EQUAL-STATS software which automatically classifies the data and performs appropriate statistical tests. EQUAL-STATS software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024)<doi:10.5281/zenodo.13354162>.

Digital simulation of electrochemical processes. Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.Update 1.2.0 improved the accuracy of the measurements and allow personalized data resolution for simulation. Bibliography regarding this methods can be found in the following texts. Dieter Britz, Jorg Strutwolf (2016) <ISBN:978-3-319-30292-8>. Allen J. Bard, Larry R. Faulkner (2000) <ISBN:978-0-471-04372-0>.

An implementation of multiple-locus association mapping on a genome-wide scale. Eagle can handle inbred and outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since Eagle is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. Eagle reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run OpenGUI()'. This opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.

This package performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>. Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) <doi:10.1007/s10687-007-0034-2>, the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and a similar approach of Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3> that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided.

Analysis of trade in value added with international input-output tables. Includes commands for easy data extraction, matrix manipulation, decomposition of value added in gross exports and calculation of value added indicators, with full geographical and sector customization. Decomposition methods include Borin and Mancini (2023) <doi:10.1080/09535314.2022.2153221>, Miroudot and Ye (2021) <doi:10.1080/09535314.2020.1730308>, Wang et al. (2013) <https://econpapers.repec.org/paper/nbrnberwo/19677.htm> and Koopman et al. (2014) <doi:10.1257/aer.104.2.459>.