R-universe search: needs:RPushbullet

melissagwolf

dynamic:DFI Cutoffs for Latent Variable Models

Returns dynamic fit index (DFI) cutoffs for latent variable models that are tailored to the user's model statement, model type, and sample size. This is the counterpart of the Shiny Application, <https://dynamicfit.app>.

Maintained by Melissa G. Wolf. Last updated 3 months ago.

16 stars 7.13 score 139 scripts

simnph

SimNPH:Simulate Non-Proportional Hazards

A toolkit for simulation studies concerning time-to-event endpoints with non-proportional hazards. 'SimNPH' encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the 'SimDesign' package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) <doi:10.1002/sim.70019>.

Maintained by Tobias Fellinger. Last updated 28 days ago.

clinical-trial-simulations non-proportional-hazards statistical-simulation statistics survival-analysis

6 stars 6.63 score 43 scripts

cotima

CoTiMA:Continuous Time Meta-Analysis ('CoTiMA')

The 'CoTiMA' package performs meta-analyses of correlation matrices of repeatedly measured variables taken from studies that used different time intervals. Different time intervals between measurement occasions impose problems for meta-analyses because the effects (e.g. cross-lagged effects) cannot be simply aggregated, for example, by means of common fixed or random effects analysis. However, continuous time math, which is applied in 'CoTiMA', can be used to extrapolate or intrapolate the results from all studies to any desired time lag. By this, effects obtained in studies that used different time intervals can be meta-analyzed. 'CoTiMA' fits models to empirical data using the structural equation model (SEM) package 'ctsem', the effects specified in a SEM are related to parameters that are not directly included in the model (i.e., continuous time parameters; together, they represent the continuous time structural equation model, CTSEM). Statistical model comparisons and significance tests are then performed on the continuous time parameter estimates. 'CoTiMA' also allows analysis of publication bias (Egger's test, PET-PEESE estimates, zcurve analysis etc.) and analysis of statistical power (post hoc power, required sample sizes). See Dormann, C., Guthier, C., & Cortina, J. M. (2019) <doi:10.1177/1094428119847277>. and Guthier, C., Dormann, C., & Voelkle, M. C. (2020) <doi:10.1037/bul0000304>.

Maintained by Markus Homberg. Last updated 8 days ago.

4 stars 5.26 score

choi-phd

lordif:Logistic Ordinal Regression Differential Item Functioning using IRT

Performs analysis of Differential Item Functioning (DIF) for dichotomous and polytomous items using an iterative hybrid of ordinal logistic regression and item response theory (IRT) according to Choi, Gibbons, and Crane (2011) <doi:10.18637/jss.v039.i08>.

Maintained by Seung W. Choi. Last updated 3 months ago.

1 stars 5.12 score 35 scripts 1 dependents

tspsyched

autoFC:Automatic Construction 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 <doi:10.1037/apl0000414>). 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 <doi:10.1177/1094428119836486>). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 <doi:10.31234/osf.io/hmnrc>). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) or minimize item location differences (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests (Li et al., 2022 <doi:10.1177/01466216211051726>), essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC tests can be automatically constructed based on user-defined pairing criteria and weights as well as customized optimization behavior. Users can also construct parallel forms of the same test following the same pairing rules.

Maintained by Mengtong Li. Last updated 21 days ago.

4 stars 4.90 score 3 scripts

micbtz

equateIRT:IRT Equating Methods

Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, Battauz (2015) <doi:10.18637/jss.v068.i07>). Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) <doi:10.1007/s10260-018-00442-w>). The package includes tests to assess the stability of the equating transformations (Battauz(2022) <doi:10.1111/stan.12277>).

Maintained by Michela Battauz. Last updated 6 months ago.

4.70 score 35 scripts 2 dependents

sooyongl

flps:Fully-Latent Principal Stratification

Simulation and analysis of Fully-Latent Principal Stratification (FLPS) with measurement models. Lee, Adam, Kang, & Whittaker (2023). <doi:10.1007/978-3-031-27781-8_25>. This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.

Maintained by Sooyong Lee. Last updated 12 months ago.

2 stars 4.58 score 19 scripts

flxzimmer

irtpwr:Power Analysis for IRT Models Using the Wald, LR, Score, and Gradient Statistics

Implementation of analytical and sampling-based power analyses for the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied to item response theory (IRT) models that are fitted using marginal maximum likelihood estimation. The methods are described in our paper (Zimmer et al. (2022) <doi:10.1007/s11336-022-09883-5>).

Maintained by Felix Zimmer. Last updated 1 years ago.

1 stars 4.30 score 9 scripts

sevvandi

airt:Evaluation of Algorithm Collections Using Item Response Theory

An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.

Maintained by Sevvandi Kandanaarachchi. Last updated 1 years ago.

4.18 score 8 scripts 1 dependents

ddueber

BifactorIndicesCalculator:Bifactor Indices Calculator

The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the 'Excel' based 'Bifactor Indices Calculator' (Dueber, 2017) <doi:10.13023/edp.tool.01> with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models.

Maintained by David Dueber. Last updated 2 months ago.

8 stars 3.90 score 7 scripts

jorgetendeiro

PerFit:Person Fit

Several person-fit statistics (PFSs; Meijer and Sijtsma, 2001, <doi:10.1177/01466210122031957>) are offered. These statistics allow assessing whether individual response patterns to tests or questionnaires are (im)plausible given the other respondents in the sample or given a specified item response theory model. Some PFSs apply to dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs (personal biserial correlation, caution index, (normed) number of Guttman errors, agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include extensions of lz, U3, and (normed) number of Guttman errors.

Maintained by Jorge N. Tendeiro. Last updated 3 years ago.

1 stars 3.36 score 46 scripts

cran

difR:Collection of Methods to Detect Dichotomous Differential Item Functioning (DIF)

Provides a collection of standard methods to detect differential item functioning among dichotomously scored items. Methods for uniform and non-uniform DIF, based on test-score or IRT methods, for comparing two or more than two groups of respondents, are available (Magis, Beland, Tuerlinckx and De Boeck,A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning, Behavior Research Methods, 42, 2010, 847-862 <doi:10.3758/BRM.42.3.847>).

Maintained by David Magis. Last updated 5 years ago.

3.33 score 6 dependents

mmansolf

AlignLV:Multiple Group Item Response Theory Alignment Helpers for 'lavaan' and 'mirt'

Allows for multiple group item response theory alignment a la 'Mplus' to be applied to lists of single-group models estimated in 'lavaan' or 'mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments.

Maintained by Maxwell Mansolf. Last updated 6 months ago.

3.00 score 2 scripts

nilsmy

jrt:Item Response Theory Modeling and Scoring for Judgment Data

Psychometric analysis and scoring of judgment data using polytomous Item-Response Theory (IRT) models, as described in Myszkowski and Storme (2019) <doi:10.1037/aca0000225> and Myszkowski (2021) <doi:10.1037/aca0000287>. A function is used to automatically compare and select models, as well as to present a variety of model-based statistics. Plotting functions are used to present category curves, as well as information, reliability and standard error functions.

Maintained by Nils Myszkowski. Last updated 2 years ago.

2.70 score 9 scripts

micbtz

equateMultiple:Equating of Multiple Forms

Equating of multiple forms using Item Response Theory (IRT) methods (Battauz M. (2017) <doi:10.1007/s11336-016-9517-x>, Battauz and 'Leoncio' (2023) <doi:10.1177/01466216231151702>, Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>).

Maintained by Michela Battauz. Last updated 6 months ago.

openblas cpp

2.48 score 6 scripts

aybekec

RSP:'shiny' Applications for Statistical and Psychometric Analysis

Toolbox with 'shiny' applications for widely used psychometric methods. Those methods include following analysis: Item analysis, item response theory calibration, principal component analysis, confirmatory factor analysis - structural equation modeling, generating simulated data. References: Chalmers (2012, <doi:10.18637/jss.v048.i06>); Revelle (2022, <https://CRAN.R-project.org/package=psych Version = 2.2.9.>); Rosseel (2012, <doi:10.18637/jss.v048.i02>); Magis & Raiche (2012, <doi:10.18637/jss.v048.i08>); Magis & Barrada (2017, <doi:10.18637/jss.v076.c01>).

Maintained by Eren Can Aybek. Last updated 2 years ago.

openjdk

2.28 score 19 scripts

santosoph

sphereML:Analyzing Students' Performance Dataset in Physics Education Research (SPHERE) using Machine Learning (ML)

A shiny R package facilitating ML based analysis for physics education research (PER) purposes. The data has been made available in the CRAN repository through the spheredata package. SPHERE stands for Students' Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at high school curriculum. We follow the stream of multidimensional students' assessment as probed by some research based assessments (RBAs) in PER. The goal is to predict the students' performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables influencing students' performance on physics are also demonstrated. We provide teachers' judgment data as our baseline to compare the predictive results of students' performance between machine learning (ML) based analysis and teacher (human) based judgment. Click on the Tab below to explore the detail of each data further.

Maintained by Purwoko Haryadi Santoso. Last updated 4 months ago.

2.00 score

eda-akdogdu

ConvertPar:Estimating IRT Parameters via Machine Learning Algorithms

A tool to estimate IRT item parameters (2 PL) using CTT-based item statistics from small samples via artificial neural networks and regression trees.

Maintained by Eda Akdogdu Yildiz. Last updated 3 years ago.

openjdk

2.00 score

cran

mstDIF:A Collection of DIF Tests for Multistage Tests

A collection of statistical tests for the detection of differential item functioning (DIF) in multistage tests. Methods entail logistic regression, an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests. The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform DIF effects are available depending on which method is used.

Maintained by Rudolf Debelak. Last updated 7 months ago.

2.00 score

bpoconnor

EFA.dimensions:Exploratory Factor Analysis Functions for Assessing Dimensionality

Functions for eleven procedures for determining the number of factors, including functions for parallel analysis and the minimum average partial test. There are also functions for conducting principal components analysis, principal axis factor analysis, maximum likelihood factor analysis, image factor analysis, and extension factor analysis, all of which can take raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, gamma correlations, or polychoric correlations. Varimax rotation, promax rotation, and Procrustes rotations can be performed. Additional functions focus on the factorability of a correlation matrix, the congruences between factors from different datasets, the assessment of local independence, the assessment of factor solution complexity, and internal consistency. Auerswald & Moshagen (2019, ISSN:1939-1463); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2); Mulaik (2010, ISBN:978-1-4200-9981-2); O'Connor (2000, <doi:10.3758/bf03200807>); O'Connor (2001, ISSN:0146-6216).

Maintained by Brian P. OConnor. Last updated 10 months ago.

1 stars 1.57 score 33 scripts

huseyinyildiz35

irtGUI:Item Response Theory Analysis with a Graphic User Interface

Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface.

Maintained by Huseyin Yildiz. Last updated 4 years ago.

1.00 score

cran

irtreliability:Item Response Theory Reliability

Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.

Maintained by Bjorn Andersson. Last updated 7 years ago.

1.00 score

huseyinyildiz35

ctgdist:Likert Category Distance Calculator

It is assumed that psychological distances between the categories are equal for the measurement instruments consisted of polytomously scored items. According to Muraki, this assumption must be tested. In the examination process of this assumption, the fit indexes are obtained and evaluated. This package provides that this assumption is removed. By with this package, the converted scale values of all items in a measurement instrument can be calculated by estimating a category parameter set for each item. Thus, the calculations can be made without any need to usage of the common category parameter set. Through this package, the psychological distances of the items are scaled. The scaling of a category parameter set for each item cause differentiation of score of the categories will be got from items. Also, the total measurement instrument score of an individual can be calculated according to the scaling of item score categories by with this package.This package provides that the place of individuals related to the structure to be measured with a measurement instrument consisted of polytomously scored items can be reveal more accurately. In this way, it is thought that the results obtained about individuals can be made more sensitive, and the differences between individuals can be revealed more accurately. On the other hand, it can be argued that more accurate evidences can be obtained regarding the psychometric properties of the measurement instruments.

Maintained by Huseyin Yildiz. Last updated 5 years ago.

1.00 score