This package provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models. As a special case of the EBglmnet package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.

This package contains methods for observed-score linking and equating under the single-group, equivalent-groups, and nonequivalent-groups with anchor test(s) designs. Equating types include identity, mean, linear, general linear, equipercentile, circle-arc, and composites of these. Equating methods include synthetic, nominal weights, Tucker, Levine observed score, Levine true score, Braun/Holland, frequency estimation, and chained equating. Plotting and summary methods, and methods for multivariate presmoothing and bootstrap error estimation are also provided.

Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are â equivalentâ to those in the control group. Here, â equivalenceâ means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi:10.1080/07350015.2024.2308121>.

Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities.

Package implements the EDNE-test for equivalence according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>. "EDNE" abbreviates "Euclidean Distance between the Non-standardized Expected values". The EDNE-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Euclidean distance. The test is an asymptotically valid test for the family of distributions fulfilling the assumptions of the multivariate central limit theorem (see Hoffelder et al.,2015). The function EDNE.EQ() implements the EDNE-test for equivalence according to Hoffelder et al. (2015). The function EDNE.EQ.dissolution.profiles() implements a variant of the EDNE-test for equivalence analyses of dissolution profiles (see Suarez-Sharp et al.,2020 <DOI:10.1208/s12248-020-00458-9>). EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the differences of the expected values of both dissolution profile populations is statistically significantly smaller than 10 [\% of label claim]. The current regulatory standard approach for equivalence analyses of dissolution profiles is the similarity factor f2. The statistical hypotheses underlying EDNE.EQ.dissolution.profiles() coincide with the hypotheses for f2 (see Hoffelder et al.,2015, Suarez-Sharp et al., 2020).

Compute common data quality metrics for accuracy, precision and data loss for screen-based eye trackers. Supports input data both in pixels on the screen and in degrees, output measures are (where appropriate) expressed as angles in degrees.

This package implements several algorithms for bundling edges in networks and flow and metro map layouts. This includes force directed edge bundling <doi:10.1111/j.1467-8659.2009.01450.x>, a flow algorithm based on Steiner trees<doi:10.1080/15230406.2018.1437359> and a multicriteria optimization method for metro map layouts <doi:10.1109/TVCG.2010.24>.

Model-based clustering for paired data based on the regression of a mixture of Bayesian hierarchical models on covariates. Zhang et al. (2023) <doi:10.1186/s12859-023-05556-x>.

The basic use of this package is with 3 sequential functions. First to generate a cell mean matrix. In case of a repeated measurements design also generate correlation and covariance matrices. This is followed by iterative experiment simulation. Finally, power is calculated from the simulated data. Features that may be considered in the model are interaction, measure correlation, non-normal and unbalanced designs distributions.

Evaluate a function over a data frame of expressions.

Perform a Bayesian estimation of the exploratory reduced reparameterized unified model (ErRUM) described by Culpepper and Chen (2018) <doi:10.3102/1076998618791306>.

This package contains a collection of examples of evidence factors in observational studies from the book Replication and Evidence Factors in Observational Studies by Paul R. Rosenbaum (2021) <doi:10.1201/9781003039648>.

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.

Constructs a shiny app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.

Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles, mean comparisons using weighted effect coding, generalized linear models, and linear multilevel models) in complex survey designs comprising multiple imputed or nested imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. See the package documentation for a more detailed description along with references.

Various Expectation-Maximization (EM) algorithms are implemented for item response theory (IRT) models. The package includes IRT models for binary and ordinal responses, along with dynamic and hierarchical IRT models with binary responses. The latter two models are fitted using variational EM. The package also includes variational network and text scaling models. The algorithms are described in Imai, Lo, and Olmsted (2016) <DOI:10.1017/S000305541600037X>.

If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups. The exterior match is used to compare the two control groups.

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>.

This package provides a framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). You create an ODE model, tells ecode to explore its behaviour, and perform numerical simulations on the model. ecode also allows you to fit model parameters by machine learning algorithms. Potential users include researchers who are interested in the dynamics of ecological community and biogeochemical cycles.

Descriptive analysis is essential for publishing medical articles. This package provides an easy way to conduct the descriptive analysis. 1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test. 2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, Tukey for the numeric variables and FDR for the factor variables. 3. T test, ANOVA or Fisher test can be forced to apply. 4. Mean and standard deviation can be forced to display.

This package implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) <doi:10.1093/aje/kwt133>.

This package provides a comprehensive collection of utility functions for data analysis and visualization in R. The package provides 55+ functions for data manipulation, file handling, color palette management, bioinformatics workflows, plotting, and package management. Features include void value handling, custom infix operators, flexible file I/O, and publication-ready visualizations with sensible defaults. Implementation follows tidyverse principles (Wickham et al. (2019) <doi:10.21105/joss.01686>) and incorporates best practices from the R community.

This package provides tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) <doi:10.1093/aje/kwh255>.

Generates feature matrix outputs from R object inputs using a variety of expansion functions. The generated feature matrices have applications as inputs for a variety of machine learning algorithms. The expansion functions are based on coercing the input to a matrix, treating the columns as features and converting individual columns or combinations into blocks of columns. Currently these include expansion of columns by efficient sparse embedding by vectors of lags, quadratic expansion into squares and unique products, powers by vectors of degree, vectors of orthogonal polynomials functions, and block random affine projection transformations (RAPTs). The transformations are magrittr- and cbind-friendly, and can be used in a building block fashion. For instance, taking the cos() of the output of the RAPT transformation generates a stationary kernel expansion via Bochner's theorem, and this expansion can then be cbind-ed with other features. Additionally, there are utilities for replacing features, removing rows with NAs, creating matrix samples of a given distribution, a simple wrapper for LASSO with CV, a Freeman-Tukey transform, generalizations of the outer function, matrix size-preserving discrete difference by row, plotting, etc.