This package provides a profile boosting framework for feature selection in parametric models. It offers a unified interface pboost() and several wrapped models, including linear model, generalized linear models, quantile regression, Cox proportional hazards model, beta regression. An S3 interface EBIC() is provided as the stopping rule for the profile boosting by default.

This package provides tools for exploring projection pursuit classification tree using various projection pursuit indexes.

Presentation two independence tests for two-way, three-way and four-way contingency tables. These tests are: the modular test and the logarithmic minimum test. For details on this method see: Sulewski (2017) <doi:10.18778/0208-6018.330.04>, Sulewski (2018) <doi:10.1080/02664763.2018.1424122>, Sulewski (2019) <doi:10.2478/bile-2019-0003>, Sulewski (2021) <doi:10.1080/00949655.2021.1908286>.

Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.

This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.

Manipulates invertible functions from a finite set to itself. Can transform from word form to cycle form and back. To cite the package in publications please use Hankin (2020) "Introducing the permutations R package", SoftwareX, volume 11 <doi:10.1016/j.softx.2020.100453>.

This package implements the methods proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>, Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>, Ackerberg, D.A. and Caves, K. and Frazer, G. (2015) <doi:10.3982/ECTA13408> and Wooldridge, J.M. (2009) <doi:10.1016/j.econlet.2009.04.026> for structural productivity estimation .

Fast estimation of binomial spatial probit regression models with spatial autocorrelation for big datasets.

This package provides a doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.

This package implements the Phylogeny-Guided Microbiome OTU-Specific Association Test method, which boosts the testing power by adaptively borrowing information from phylogenetically close OTUs (operational taxonomic units) of the target OTU. This method is built on a kernel machine regression framework and allows for flexible modeling of complex microbiome effects, adjustments for covariates, and can accommodate both continuous and binary outcomes.

Analyses and reports questionnaire and experiment data exported from PsyToolkit'. The package reads downloaded study folders, parses questionnaire structure, optionally merges demographic exports from CloudResearch or Prolific, and produces summary overviews of responses and completion times. It also provides helper functions to extract and aggregate experiment measures and survey variables, and to export results to spreadsheet files for further analysis and archiving. See Stoet (2017) <doi:10.1177/0098628316677643> for the PsyToolkit platform.

Data about Disney Pixar films provided by Wikipedia. This package contains data about the films, the people involved, and their awards.

Fit linear splines to species time series to detect population growth scenarios based on Hyndman, R J and Mesgaran, M B and Cousens, R D (2015) <doi:10.1007/s10530-015-0962-8>.

This package provides functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.

This package provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.

Recent years have seen an increased interest in novel methods for analyzing quantitative data from experimental psychology. Currently, however, they lack an established and accessible software framework. Many existing implementations provide no guidelines, consisting of small code snippets, or sets of packages. In addition, the use of existing packages often requires advanced programming experience. PredPsych is a user-friendly toolbox based on machine learning predictive algorithms. It comprises of multiple functionalities for multivariate analyses of quantitative behavioral data based on machine learning models.

This package provides a cohesive framework for the spectral and spatial analysis of colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) <doi:10.1111/2041-210X.12069> and Maia, Gruson, Endler & White (2019) <doi:10.1111/2041-210X.13174>.

This package provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages.

Explore various dependencies of a packages (on the Comprehensive R Archive Network Like repositories). The functions get_neighborhood() and get_dependencies() provide dependencies of packages and as_graph() can be used to convert into a igraph object for further analysis and plotting.

This package contains a graphical user interface to generate the diagnostic plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate nonlinear bivariate relationships in latent regression models using structural equation mixture models (SEMMs).

Sample size calculations in causal inference with observational data are increasingly desired. This package is a tool to calculate sample size under prespecified power with minimal summary quantities needed.

Utilize the CDF penalty function to estimate a penalized linear model. It enables you to display some graphical representations and determine whether the Karush-Kuhn-Tucker conditions are met. For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) <arXiv:2212.08582>.

Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) <doi:10.1007/b98835>, Gabriel (1971) <doi:10.1093/biomet/58.3.453>, Zhang et al. (2024) <doi:10.1038/s41467-024-53239-9>, and Anandan et al. (2022) <doi:10.1038/s41598-022-07781-5>.

An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). One function estimates percentile differences from two percentiles while the other returns the values for every percentile from 1 to 100.