You can use the set of wrappers for analytical schemata to reduce the effort in writing machine-readable data. The set of all-in-one wrappers will cover widely used functions from data analysis packages.

Defines predict function that transforms output from a Tweedie Generalized Linear Mixed Model (using glmmTMB'), Generalized Additive Model (using mgcv'), or spatio-temporal Generalized Linear Mixed Model (using package tinyVAST'), and returns predicted proportions (and standard errors) across a grouping variable from an equivalent multivariate-logit Tweedie model. These predicted proportions can then be used for standard plotting and diagnostics. See Thorson et al. 2022 <doi:10.1002/ecy.3637>.

This package provides functions for estimating structural equation models using instrumental variables.

This package provides functions to run fixed effects or random effects multivariate meta-analysis.

Read a table of fixed width formatted data of different types into a data.frame for each type.

Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>. It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.

Values below the limit of detection (LOD) are a problem in several fields of science, and there are numerous approaches for replacing the missing data. We present a new mathematical solution for maximum likelihood estimation that allows us to estimate the true values of the mean and standard deviation for normal distributions and is significantly faster than previous implementations. The article with the details was submitted to JSS and can be currently seen on <https://www2.arnes.si/~tverbo/LOD/Verbovsek_Sega_2_Manuscript.pdf>.

This package implements three bias-correction techniques from Battaglia et al. (2025 <doi:10.48550/arXiv.2402.15585>) to improve inference in regression models with covariates generated by AI or machine learning.

This package provides flexible dictionary-based cleaning that allows users to specify implicit and explicit missing data, regular expressions for both data and columns, and global matches, while respecting ordering of factors. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Framework for creating and orchestrating data pipelines. Organize, orchestrate, and monitor multiple pipelines in a single project. Use tags to decorate functions with scheduling parameters and configuration.

Run the same analysis over a range of arbitrary data processing decisions. multitool provides an interface for creating alternative analysis pipelines and turning them into a grid of all possible pipelines. Using this grid as a blueprint, you can model your data across all possible pipelines and summarize the results.

Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold. The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator. Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.

This package provides a set of easy-to-use functions for computing the Multidimensional Poverty Index (MPI).

Apply the marginal classification method to achieve the purpose of providing the point and interval estimates for the minimal clinically important difference based on the classical anchor-based method. For more details of the methodology, please see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.

This package provides tools for training, selecting, and evaluating maximum entropy (and standard logistic regression) distribution models. This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum- likelihood interpretation of maximum entropy modeling, and uses infinitely- weighted logistic regression for model fitting. The package is described in Vollering et al. (2019; <doi:10.1002/ece3.5654>).

This package provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.

We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.

More data sets used for demonstrating or testing model-related packages are contained in this package. The data sets are downloaded and cached, allowing for more and bigger data sets.

Implementation of custom tidymodels metrics for multi-class prediction models with a single negative class. Currently are implemented macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020) "Imbalance accuracy metric for model selection in multi-class imbalance classification problemsâ <doi:10.1016/j.knosys.2020.106490> and a generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng J.X and Chen P.Y. (2013) â Weighted Youden index and its two-independent-sample comparison based on weighted sensitivity and specificityâ <doi:10.3760/cma.j.issn.0366-6999.20123102>.

Find common entities detected in both positive and negative ionization mode, delete this entity in the less sensible mode and combine both matrices.

The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.

An implementation of the Super Learner prediction algorithm from van der Laan, Polley, and Hubbard (2007) <doi:10.2202/1544-6115.1309 using the mlr3 framework.

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').

Make all elements of a character vector unique. Differs from make.unique by starting at 1 and allowing users to customise suffix format.