Takes user-provided baseline data from groups of randomised controlled data and assesses whether the observed distribution of baseline p-values, numbers of participants in each group, or categorical variables are consistent with the expected distribution, as an aid to the assessment of integrity concerns in published randomised controlled trials. References (citations in PubMed format in details of each function): Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) <doi:10.1212/WNL.0000000000003387>. Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) <doi:10.1016/j.jclinepi.2019.05.006>. Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) <doi:10.1016/j.jclinepi.2019.03.001>. Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) <doi:10.1111/anae.15165>. Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) <doi:10.1016/j.jclinepi.2020.11.012>. Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) <doi:10.1016/j.jclinepi.2021.05.002>. Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) <doi:10.1016/j.jclinepi.2022.12.018>. Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>. Carlisle JB. (2017) <doi:10.1111/anae.13938>.

This package provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

This package provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R., and Gupta, V. K. (2013) <doi:10.1007/s40003-013-0059-5>.

This package provides a robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.

Build reusable validators from small building blocks using the base pipe operator. Define runtime contracts once with restrict() and enforce them anywhere in code. Validators compose naturally, support dependent rules via formulas, and produce clear, path-aware error messages. No DSL, no operator overloading, just idiomatic R.

This package contains functions useful for reading in Licor 6800 files, correcting and analyzing rapid A/Ci response (RACiR) data. Requires some user interaction to adjust the calibration (empty chamber) data file to a useable range. Calibration uses a 1st to 5th order polynomial as suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>. Data can be processed individually or batch processed for all files paired with a given calibration file. RACiR is a trademark of LI-COR Biosciences, and used with permission.

This package provides a checkbox group input for usage in a Shiny application. The checkbox group has a head checkbox allowing to check or uncheck all the checkboxes in the group. The checkboxes are customizable.

Convex Least Squares Programming (CLSP) is a two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. It combines pseudoinverse-based estimation with convex-programming correction methods inspired by Lasso, Ridge, and Elastic Net to ensure numerical stability, constraint enforcement, and interpretability. The package also provides numerical stability analysis and CLSP-specific diagnostics, including partial R^2, normalized RMSE (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based confidence bands.

This package provides an interface to vinecopulib', a C++ library for vine copula modeling. The rvinecopulib package implements the core features of the popular VineCopula package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over VineCopula are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The rvinecopulib package includes vinecopulib as header-only C++ library (currently version 0.7.2). Thus users do not need to install vinecopulib itself in order to use rvinecopulib'. Since their initial releases, vinecopulib is licensed under the MIT License, and rvinecopulib is licensed under the GNU GPL version 3.

Designed to streamline data analysis and statistical testing, reducing the length of R scripts while generating well-formatted outputs in pdf', Microsoft Word', and Microsoft Excel formats. In essence, the package contains functions which are sophisticated wrappers around existing R functions that are called by using f_ (user f_riendly) prefix followed by the normal function name. This third version of the rfriend package focuses primarily on data exploration, including tools for creating summary tables, f_summary(), summary figures, f_scan(), outlier detection and removal, f_outlier() and f_remove_outliers(), performing data transformations, f_boxcox() in part based on MASS/boxcox and rcompanion', and f_bestNormalize() which wraps and extends functionality from the bestNormalize package. Furthermore, rfriend can automatically (or on request) generate visualizations such as boxplots, f_boxplot(), QQ-plots, f_qqnorm(), histograms f_hist(), and density plots f_density(). Additionally, the package includes several statistical test functions: f_aov(), f_chisq_test(), f_corplot(), f_kruskal_test(), f_lmer(), f_glm(), f_t_test(), f_wilcox_test(), for sequential testing and visualisation of the similar named stats functions. These functions, except for f_chisq_test(), support testing multiple response variables and predictors, while also handling assumption checks, data transformations, and post hoc tests. Post hoc results are automatically summarized in a table using the compact letter display (cld) format for easy interpretation. The package also provides a function to do model comparison, f_model_comparison(), and several utility functions to simplify common R tasks. For example, f_clear() clears the workspace and restarts R with a single command; f_setwd() sets the working directory to match the directory of the current script; f_theme() quickly changes RStudio themes; and f_factors() converts multiple columns of a data frame to factors, and much more. If you encounter any issues or have feature requests, please feel free to contact me via email.

Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), <doi:10.1146/annurev-statistics-060116-054035>.

Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.

Converts elements of roxygen documentation to markdown'.

This package provides an R interface to the RCSB Protein Data Bank ('PDB') Search and Data APIs (<https://www.rcsb.org/>). Supports full-text, attribute, sequence, motif, structure, and chemical searches; retrieval of entry-, assembly-, polymer-entity-, and chemical-component-level metadata; and conversion of API responses into analysis-ready tables and typed R objects for reproducible structural bioinformatics workflows.

It is widely documented in psychology, economics and other disciplines that socio-economic agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2020) <arXiv:1712.03448> and Cattaneo, Cheung, Ma, and Masatlioglu (2022) <arXiv:2110.10650>, which utilizes standard choice data to partially identify and estimate a decision maker's preference and attention. For inference, several simulation-based critical values are provided.

This package provides tools to automate the morphological delineation of riverside urban areas based on a method introduced in Forgaci (2018) <doi:10.7480/abe.2018.31>. Delineation entails the identification of corridor boundaries, segmentation of the corridor, and delineation of the river space using two-dimensional spatial information from street network data and digital elevation data in a projected CRS. The resulting delineation can be used to characterise spatial phenomena that can be related to the river as a central element.

This package provides a general-purpose optimisation engine that supports i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. (1953) <doi:10.1063/1.1699114>, Hastings (1970) <doi:10.1093/biomet/57.1.97>] and Acceptance Ratio Simulated Annealing [Kirkpatrick et al. (1983) <doi:10.1126/science.220.4598.671>, Ä erný (1985) <doi:10.1007/BF00940812>] on multiple cores, and ii) Acceptance Ratio Replica Exchange Monte Carlo Optimisation. In each case, the system pseudo-temperature is dynamically adjusted such that the observed acceptance ratio is kept near to the desired (fixed or changing) acceptance ratio.

This package contains example data for the rehh package.

Set of functions for Regression Discontinuity Design ('RDD'), for data visualisation, estimation and testing.

Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and RStudio Viewer. r3dmol includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the 3dmol.js and the htmlwidgets R package.

This R package connects to SWI-Prolog, <https://www.swi-prolog.org/>, so that R can send deterministic and non-deterministic queries to prolog (consult, query/submit, once, findall).

Generate utils::globalVariables() from roxygen2 @global and @autoglobal tags.

This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: <https://www.monash.edu/business/ebs/research/publications/ebs/wp16-2019.pdf>.