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Open AccessArticle

Easing the Hubble Tension in f(R,L_m) Gravity: A Bayesian MCMC Analysis with CC and Pantheon Plus & SH0ES Datasets

by Archana Dixit, Saurabh Verma, Anirudh Pradhan and M. S. Barak

Universe 2026, 12(3), 66; https://doi.org/10.3390/universe12030066 - 27 Feb 2026

Viewed by 337

In this study, we explored the cosmological implications of the modified gravity framework

f (R, L_{m})

, taking the specific form

f (R, L_{m}) = \frac{R}{2} + L_{m}^{n},

where n denotes [...] Read more.

In this study, we explored the cosmological implications of the modified gravity framework

f (R, L_{m})

, taking the specific form

f (R, L_{m}) = \frac{R}{2} + L_{m}^{n},

where n denotes the model parameter. The analysis was carried out within a spatially flat FLRW background by adopting the Barboza–Alcaniz (BA) parametrization for the dark energy equation of state, expressed as

ω (z) = w_{0} + w_{1} \frac{z (1 + z)}{1 + z^{2}} .

Based on this setup, an expression for the Hubble parameter

H (z)

was derived. The parameters

(H_{0}, n, w_{0}, w_{1})

were estimated using a Bayesian Markov Chain Monte Carlo (MCMC) technique, implemented via the emcee package, with Cosmic Chronometers (CC), Pantheon Plus & SH0ES (PPS) and DESI BAO datasets. For the CC+PPS+DESI BAO combination, the best-fit Hubble constant was obtained as

H_{0} = {72.08}_{- 0.24}^{+ 0.30} km s^{- 1} {Mpc}^{- 1},

which shows better consistency with the local SH0ES measurement than with the Planck

Λ

CDM result, thereby reducing the Hubble tension. Furthermore, the dynamical evolution of the equation of state parameter

ω

, the deceleration parameter, the impact of various energy conditions, and the optimal model parameters were thoroughly examined. The study also investigated the behavior of the

(O_{m})

diagnostic and determined the present age of the universe predicted by this model. Full article

(This article belongs to the Special Issue The 10th Anniversary of Universe: Standard Cosmological Models, and Modified Gravity and Cosmological Tensions)

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24 pages, 2913 KB

Open AccessArticle

Cognitively Diagnostic Analysis Using the G-DINA Model in R

by Qingzhou Shi, Wenchao Ma, Alexander Robitzsch, Miguel A. Sorrel and Kaiwen Man

Psych 2021, 3(4), 812-835; https://doi.org/10.3390/psych3040052 - 8 Dec 2021

Cited by 21 | Viewed by 12139

Abstract

Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discussed. Full article

(This article belongs to the Special Issue Computational Aspects, Statistical Algorithms and Software in Psychometrics)

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9 pages, 566 KB

Open AccessArticle

Constraints on Non-Flat Starobinsky f(R) Dark Energy Model

by Chao-Qiang Geng, Yan-Ting Hsu and Jhih-Rong Lu

Entropy 2021, 23(10), 1320; https://doi.org/10.3390/e23101320 - 10 Oct 2021

Cited by 1 | Viewed by 2395

Abstract

We study the viable Starobinsky

f (R)

dark energy model in spatially non-flat FLRW backgrounds, where [...] Read more.

We study the viable Starobinsky

f (R)

dark energy model in spatially non-flat FLRW backgrounds, where

f (R) = R - λ R_{c h} [1 - {(1 + R^{2} / R_{c h}^{2})}^{- 1}]

with

R_{c h}

and

λ

representing the characteristic curvature scale and model parameter, respectively. We modify CAMB and CosmoMC packages with the recent observational data to constrain Starobinsky

f (R)

gravity and the density parameter of curvature

Ω_{K}

. In particular, we find the model and density parameters to be

λ^{- 1} < 0.283

at 68% C.L. and

Ω_{K} = - 0 . 00099_{- 0.0042}^{+ 0.0044}

at 95% C.L., respectively. The best

χ^{2}

fitting result shows that

χ_{f (R)}^{2} ≲ χ_{Λ C D M}^{2}

, indicating that the viable

f (R)

gravity model is consistent with

Λ

CDM when

Ω_{K}

is set as a free parameter. We also evaluate the values of AIC, BIC and DIC for the best fitting results of

f (R)

and

Λ

CDM models in the non-flat universe. Full article

(This article belongs to the Special Issue Modified Gravity: From Black Holes Entropy to Current Cosmology III)

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18 pages, 1961 KB

Open AccessArticle

cdcatR: An R Package for Cognitive Diagnostic Computerized Adaptive Testing

by Miguel A. Sorrel, Pablo Nájera and Francisco J. Abad

Psych 2021, 3(3), 386-403; https://doi.org/10.3390/psych3030028 - 9 Aug 2021

Cited by 4 | Viewed by 5186

Abstract

Cognitive diagnosis models (CDMs) are confirmatory latent class models that provide fine-grained information about skills and cognitive processes. These models have gained attention in the last few years because of their usefulness in educational and psychological settings. Recently, numerous developments have been made to allow for the implementation of cognitive diagnosis computerized adaptive testing (CD-CAT). Despite methodological advances, CD-CAT applications are still scarce. To facilitate research and the emergence of empirical applications in this area, we have developed the cdcatR package for R software. The purpose of this document is to illustrate the different functions included in this package. The package includes functionalities for data generation, model selection based on relative fit information, implementation of several item selection rules (including item exposure control), and CD-CAT performance evaluation in terms of classification accuracy, item exposure, and test length. In conclusion, an R package is made available to researchers and practitioners that allows for an easy implementation of CD-CAT in both simulation and applied studies. Ultimately, this is expected to facilitate the development of empirical applications in this area. Full article

(This article belongs to the Special Issue Computational Aspects, Statistical Algorithms and Software in Psychometrics)

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