Latent Transition Cognitive Diagnosis Model with Covariates

Implementation of the three-step approach of (latent transition) cognitive diagnosis model (CDM) with covariates. This approach can be used for single time-point situations (cross-sectional data) and multiple time-point situations (longitudinal data) to investigate how the covariates are associated with attribute mastery. For multiple time-point situations, the three-step approach of latent transition CDM with covariates allows researchers to assess changes in attribute mastery status and to evaluate the covariate effects on both the initial states and transition probabilities over time using latent logistic regression. Because stepwise approaches often yield biased estimates, correction for classification error probabilities (CEPs) is considered in this approach. The three-step approach for latent transition CDM with covariates involves the following steps: (1) fitting a CDM to the response data without covariates at each time point separately, (2) assigning examinees to latent states at each time point and computing the associated CEPs, and (3) estimating the latent transition CDM with the known CEPs and computing the regression coefficients. The method was proposed in Liang et al. (2023) and demonstrated using mental health data in Liang et al. (in press; annotated R code and data utilized in this example are available in Mendeley data) .

Easily Install and Load the 'Tidyverse'

The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step. Learn more about the 'tidyverse' at < https://www.tidyverse.org>.

Lasso and Elastic-Net Regularized Generalized Linear Models

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see and . There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.

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."

Estimating Length-Based Indicators for Fish Stock

Provides tools for estimating length-based indicators from length frequency data to assess fish stock status and manage fisheries sustainably. Implements methods from Cope and Punt (2009) for data-limited stock assessment and Froese (2004) for detecting overfishing using simple indicators. Key functions include: FrequencyTable(): Calculate the frequency table from the collected and also the extract the length frequency data from the frequency table with the upper length_range. A numeric value specifying the bin width for class intervals. If not provided, the bin width is automatically calculated using Wang (2020) formula. FreqTM(): Creates a frequency distribution table for fish length data across multiple months using a consistent length class structure. The bin width is determined by either a custom value or Wang's formula, applied uniformly across all months. The function dynamically detects and renames columns to 'Month' and 'Length' from the input dataframe. The maximum observed length is included as part of the last class, with the upper bound set to the smallest multiple of the bin width greater than or equal to the maximum length. Months can be converted to dates using a configurable day and year, with dates assigned sequentially in 'day.month.year' format (e.g., 15.01.26). FishPar(): Calculates length-based indicators (LBIs) proposed by Froese (2004) such as the percentage of mature fish (Pmat), percentage of optimal length fish (Popt), percentage of mega spawners (Pmega), and the sum of these as Pobj. This function also estimates confidence intervals for different lengths, visualizes length frequency distributions, and provides data frames containing calculated values. FishSS(): Makes decisions based on input from Cope and Punt (2009) and parameters calculated by FishPar() (e.g., Pobj, Pmat, Popt, LM_ratio) to determine stock status as target spawning biomass (TSB40) and limit spawning biomass (LSB25), and selectivity. LWR(): Fits and visualizes length-weight relationships using linear regression, with options for log-transformation and customizable plotting.

Extended Model Formulas

Infrastructure for extended formulas with multiple parts on the right-hand side and/or multiple responses on the left-hand side (see ).

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.

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.

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.