Convert Country Names and Country Codes

Standardize country names, convert them into one of 40 different coding schemes, convert between coding schemes, and assign region descriptors.

Assessing and Visualizing Fragility of Clinical Results

A collection of user-friendly functions for assessing fragility of clinical results with binary and survival outcomes. For binary outcomes, the package assesses and visualizes fragility of individual studies (Walsh et al., 2014 ; Lin, 2021 ), conventional pairwise meta-analyses (Atal et al., 2019 ), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 ). The functions for binary outcomes are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. For survival outcomes, the package implements the event status modification method based on the log-rank test described by Xing et al. (2026 ). It calculates the fragility index and fragility quotient for two-group studies with right-censored data, modifying event status in one or both groups while preserving follow-up times and group assignments. Results include the sequence of modifications, corresponding p-values, and an S3 print method. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 ) and detailed in Lin and Chu (2022 ).

Biological Condition Gradient, Calculator

Functions to calculate Biological Condition Gradient (BCG) using input files with one row per sample with metric values and site classes as columns. A second file with the BCG Rules (example included) to define the memberships is also needed. The three main functions convert metric scores to metric memberships following fuzzy set BCG Rules (BCG.Level.Assignment), combine metric memberships to level memberships according to BCG Rules (BCG.Level.Membership), and then assign a BCG primary and secondary level based on level memberships (BCG.Level.Assignment). Originally developed as a package for use with BCG for Puget Lowland/Willamette Valley but has been further enhanced for use with multiple communities (benthic macroinvertebrates, fish, periphyton, or coral) and different rule sets. Oregon and Washington reference - "Stamp, J. and J. Gerritsen. 2018. Calibration of the Biological Condition Gradient (BCG) for Macroinvertebrate Assemblages in Puget Lowland/Willamette Valley Freshwater Wadeable Streams. Prepared by Tetra Tech for the US EPA Office of Water, Office of Science and Technology and US EPA Region 10." BCG process documentation - "USEPA. 2016. A Practitioner’s Guide to the Biological Condition Gradient - A Framework to Describe Incremental Change in Aquatic Ecosystems. EPA 842-R-16-001. Office of Science and Technology, Washington, DC 20460."

Tools for Multiple Imputation of Missing Data

Tools to perform analyses and combine results from multiple-imputation datasets.

Simple, Multiple and Joint Correspondence Analysis

Computation and visualization of simple, multiple and joint correspondence analysis.

Comprehensive Statistical Tools for Agricultural Research

Analysis of variance for the experimental designs used in agricultural research, with the correct error term for every stratum, standard error of mean (SEm), standard error of difference (SEd), critical difference (CD), coefficient of variation (CV) and letter groupings: completely randomized design (CRD), randomized complete block design (RCBD) with missing plots, Latin square, two- and three-factor factorials, split-plot designs (including factorial main or sub-plots), split-split-plot and strip-plot designs, and combined analysis over environments. Also covers the augmented randomized complete block design with adjusted means and genetic parameters (Federer, 1961 ), alpha lattice designs with intra-block or residual maximum likelihood (REML) analysis (Patterson and Williams, 1976 ) and repeated measures with Greenhouse-Geisser and Huynh-Feldt corrections and covariance structure models. Mean comparisons (least significant difference, Duncan, Tukey, Student-Newman-Keuls, Scheffe, Bonferroni and Dunnett) follow Gomez and Gomez (1984, ISBN:0471870927), with letters assigned by the algorithm of Piepho (2004) . Stability analysis includes Eberhart and Russell (1966) regression, additive main effects and multiplicative interaction (AMMI), Finlay-Wilkinson, Shukla, Wricke, coefficient of variation and Lin-Binns methods, and genotype plus genotype-by-environment (GGE) biplots (Yan et al., 2000 ). Further tools compute growing degree days, helio-thermal and photo-thermal units, crop growth rate, relative growth rate, net assimilation rate, leaf area index and leaf area duration, harvest index, yield gaps with the technology index, nutrient use efficiency, quadratic, plateau and Mitscherlich response curves with the economic optimum dose, benefit-cost ratio, correlation, principal component, path and structural equation analysis, publication tables for many traits and figures.

Automatic Toolkit for Construction, Optimization, Scoring and Simulation of Forced-Choice Tests

Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 ). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 ). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 ). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 ) or minimize item location differences (Cao & Drasgow, 2019 ) depending on scoring models. Decision of which items should be assigned to the same block, also called as item pairing, is thus critical to the quality of an FC test. Because such pairing process often requires researchers to meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per elevates. To address these problems, autoFC is developed as a automatic and efficient tool for facilitating the automatic construction of FC tests (Li et al., 2022 ), essentially exempting users from the burden of manual item pairing. Given characteristics of each item (and item responses), FC measures can be constructed either automatically based on user-defined pairing criteria and weights, or based on exact specifications of each block (i.e., blueprint; see Li et al., 2025 ). Users can also generate simulated responses based on the Thurstonian Item Response Theory model (Brown & Maydeu-Olivares, 2011 ) and predict trait scores of simulated/actual respondents based on an estimated model.

An Object Oriented System Meant to Become a Successor to S3 and S4

A new object oriented programming system designed to be a successor to S3 and S4. It includes formal class, generic, and method specification, and a limited form of multiple dispatch. It has been designed and implemented collaboratively by the R Consortium Object-Oriented Programming Working Group, which includes representatives from R-Core, 'Bioconductor', 'Posit'/'tidyverse', and the wider R community.

Manage Environment Specific Configuration Values

Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value.

Tidying Methods for Mixed Models

Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.