This package provides a set of functions to obtain modified score test for generalized linear models.

Nonparametric unfolding item response theory (IRT) model for dichotomous data (see W.H. Van Schuur (1984). Structure in Political Beliefs: A New Model for Stochastic Unfolding with Application to European Party Activists, and W.J.Post (1992). Nonparametric Unfolding Models: A Latent Structure Approach). The package implements MUDFOLD (Multiple UniDimensional unFOLDing), an iterative item selection algorithm that constructs unfolding scales from dichotomous preferential-choice data without explicitly assuming a parametric form of the item response functions. Scale diagnostics from Post(1992) and estimates for the person locations proposed by Johnson(2006) and Van Schuur(1984) are also available. This model can be seen as the unfolding variant of Mokken(1971) scaling method.

Conducting linear and nonlinear dose-response meta-regression using study-level summary data. It supports both continuous and binary outcomes and allows modeling of dose-effect relationships using linear trends or nonlinear restricted cubic splines. The package is designed to facilitate transparent, flexible, and reproducible dose-response meta-analyses, with built-in visualization of fitted dose-response curves.

Extends the mlr3 ML framework with methods for spatial objects. Data storage and prediction are supported for packages terra', raster and stars'.

This package provides a web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the mlr3 framework.

Researchers often need to calculate body-size growth rates for individuals that do not have associated age data. These growth rates are based on mark-recapture data where an individual was captured and measured at time 1 then recaptured and measured at time 2. The sizes at each time and amount of time between captures can be used to calculate growth rates. MRgrowth follows the approach in Edmonds et al. (2021) <doi:10.1371/journal.pone.0259978> and provides functions to calculate growth using three formulas, the Faben's reformulation of the von Bertalanffy formula, the Gompertz formula, and a logistic formula.

Translate R code into MongoDB aggregation pipelines.

This package provides user-friendly methods for handling missing data in regression modeling, including available-case linear regression, multiple imputation, random-forest imputation, and the Tower method. Implemented approaches include chained-equation imputation described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>, multiple imputation described by Honaker, King and Blackwell (2011) <doi:10.18637/jss.v045.i07>, random-forest imputation described by Stekhoven and Buehlmann (2012) <doi:10.1093/bioinformatics/btr597>, and the Tower method described by Matloff and Mohanty (2023) <https://CRAN.R-project.org/package=toweranNA>.

The companion package provides all original data sets and functions that are used in the book "Model-Based Clustering and Classification for Data Science" by Charles Bouveyron, Gilles Celeux, T. Brendan Murphy and Adrian E. Raftery (2019, ISBN:9781108644181).

This package provides supplemental functions for the mixRasch package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a plotting function to compare item parameters for multiple class models and a function that provides average theta values for each class in a mixture model.

Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks.

This package provides new functions info(), warn() and error(), similar to message(), warning() and stop() respectively. However, the new functions can have a level associated with them, so that when executed the global level option determines whether they are shown or not. This allows debug modes, outputting more information. The can also output all messages to a log file.

Code to support a systems biology research program from inception through publication. The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations. The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques. As a result, we encourage suggested additions to the package, both methodological and graphical.

This package provides a flexible framework for power analysis using Monte Carlo simulation for settings in which considerations of the correlations between predictors are important. Users can set up a data generative model that preserves dependence structures among predictors given existing data (continuous, binary, or ordinal). Users can also generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This package includes several statistical models common in environmental mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.

Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>), comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>), ANCOVA (Shieh (2020), <doi:10.1007/s11336-019-09692-3>), reference ranges (Jennen-Steinmetz and Wellek (2005), <doi:10.1002/sim.2177>), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), <doi:10.1148/radiology.143.1.7063747>).

Multivariate Analysis methods and data sets used in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>. This also serves as a companion package for the STAT 571: Multivariate Analysis course offered by the Department of Statistics at the University of Illinois at Urbana-Champaign ('UIUC').

This package provides common components (classes, methods, documentation) for packages that conduct meta-analytic corrections and sensitivity analyses for within-study and/or across-study biases in meta-analysis. See the packages PublicationBias', phacking', and multibiasmeta'. These package implement methods described in, respectively: Mathur & VanderWeele (2020) <doi:10.31219/osf.io/s9dp6>; Mathur (2022) <doi:10.31219/osf.io/ezjsx>; Mathur (2022) <doi:10.31219/osf.io/u7vcb>.

Supports morphological analysis for Japanese by using MeCab <https://taku910.github.io/mecab/>, Sudachi <https://github.com/WorksApplications/Sudachi>, Chamame <https://chamame.ninjal.ac.jp/>, or Ginza <https://github.com/megagonlabs/ginza>. Can input a data.frame and obtain all results of MeCab and the row number of the original data.frame as a text id.

This package provides weighted versions of several metrics and performance measures used in machine learning, including average unit deviances of the Bernoulli, Tweedie, Poisson, and Gamma distributions, see Jorgensen B. (1997, ISBN: 978-0412997112). The package also contains a weighted version of generalized R-squared, see e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236). Furthermore, dplyr chains are supported.

This package provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.

This package provides tools for general-purpose continuous optimization and feed-forward artificial neural network training using metaheuristic and gradient-based optimization algorithms. The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, differential evolution Storn and Price (1997) <doi:10.1023/A:1008202821328>, grey wolf optimizer Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>, secretary bird optimization Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>, and Adam Kingma and Ba (2015) <doi:10.48550/arXiv.1412.6980>.

An interactive presentation on the topic of Multinomial Logistic Regression. It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at <https://jarvisatharva.shinyapps.io/MultinomPresentation>.

In the context of multistate models, which are popular in sociology, demography, and epidemiology, Markov chain with rewards calculations can help to refine transition timings and so obtain more accurate estimates. The package code accommodates up to nine transient states and irregular age (time) intervals. Traditional demographic life tables result as a special case. Formulas and methods involved are explained in detail in the accompanying article: Schneider / Myrskyla / van Raalte (2021): Flexible Transition Timing in Discrete-Time Multistate Life Tables Using Markov Chains with Rewards, MPIDR Working Paper WP-2021-002.

This package implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.