Group Sequential Operating Characteristics for Clinical, Bayesian two-arm Trials with known Sigma and Normal Endpoints, as described in Gerber and Gsponer (2016) <doi: 10.18637/jss.v069.i11>.

This package provides a system for fitting Gompertz Curve in a Time Series Data.

The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.

This package provides a convenient interface with the OpenAI ChatGPT API <https://openai.com/api>. gptr allows you to interact with ChatGPT', a powerful language model, for various natural language processing tasks. The gptr R package makes talking to ChatGPT in R super easy. It helps researchers and data folks by simplifying the complicated stuff, like asking questions and getting answers. With gptr', you can use ChatGPT in R without any hassle, making it simpler for everyone to do cool things with language!

This package contains functions to create life history parameter plots from raw data. The plots are created using ggplot2', and calculations done using the tidyverse collection of packages. The package contains references to FishBase (Froese R., Pauly D., 2023) <https://www.fishbase.se/>.

Parameter estimation for the general class of semiparametric models for recurrent event data proposed by Peña and Hollander (2004, <ISBN:978-1-4020-7737-6>). The model incorporates an effective age function encoding the impact of interventions after each event occurrence, the effect of accumulating event occurrences, a link function for possibly time-dependent covariates, and optional gamma frailties to induce dependence among inter-event times. It also fits the extension for cancer relapses of González et al. (2005) <doi:10.1002/sim.2410>. Estimation is performed by profile likelihood, with an expectation-maximization algorithm for the frailty model, and the package provides descriptive, diagnostic and predictive tools for the fitted models.

This package implements methods to plot periodic data in any arbitrary range on the fly.

Build graphs for landscape genetics analysis. This set of functions can be used to import and convert spatial and genetic data initially in different formats, import landscape graphs created with GRAPHAB software (Foltete et al., 2012) <doi:10.1016/j.envsoft.2012.07.002>, make diagnosis plots of isolation by distance relationships in order to choose how to build genetic graphs, create graphs with a large range of pruning methods, weight their links with several genetic distances, plot and analyse graphs, compare them with other graphs. It uses functions from other packages such as adegenet (Jombart, 2008) <doi:10.1093/bioinformatics/btn129> and igraph (Csardi et Nepusz, 2006) <https://igraph.org/>. It also implements methods commonly used in landscape genetics to create graphs, described by Dyer et Nason (2004) <doi:10.1111/j.1365-294X.2004.02177.x> and Greenbaum et Fefferman (2017) <doi:10.1111/mec.14059>, and to analyse distance data (van Strien et al., 2015) <doi:10.1038/hdy.2014.62>.

Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.

Fit generalized linear mixed models (GLMMs) with normal random effects using first-order Laplace, fully exponential Laplace (FEL) with mean-only corrections, and FEL with mean and variance-diagonal corrections in the E-step of an expectation-maximization (EM) algorithm. The current development version provides a matrix-based interface (y, X, Z) and supports binary logit and probit, and Poisson log-link models. An EM framework is used to update fixed effects, random effects, and a single variance component tau^2 for G = tau^2 I, with staged approximations (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A pseudo-likelihood engine glmmFEL_pl() implements the working-response / working-weights linearization approach of Wolfinger and O'Connell (1993) <doi:10.1080/00949659308811554>, and is adapted from the implementation used in the RealVAMS package (Broatch, Green, and Karl (2018)) <doi:10.32614/RJ-2018-033>. The FEL implementation follows Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019> and related work (e.g., Tierney, Kass, and Kadane (1989) <doi:10.1080/01621459.1989.10478824>; Rizopoulos, Verbeke, and Lesaffre (2009) <doi:10.1111/j.1467-9868.2008.00704.x>; Steele (1996) <doi:10.2307/2532845>). Package code was drafted with assistance from generative AI tools.

The web service at <https://www.geonames.org/> provides a number of spatial data queries, including administrative area hierarchies, city locations and some country postal code queries. A (free) username is required and rate limits exist.

This package implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi: 10.1080/10705511.2014.915380>. And, it can also implement AIPE on RMSEA and power analysis on RMSEA.

For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.

This package implements statistical methods for group factor analysis, focusing on estimating the number of global and local factors and extracting them. Several algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008>, Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the Aggregated Projection Method by Hu et al. (2025) <doi:10.1080/01621459.2025.2491154>.

This package provides functions for implementing the Generalized Bayesian Optimal Phase II (G-BOP2) design using various Particle Swarm Optimization (PSO) algorithms, including: - PSO-Default, based on Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, "Particle Swarm Optimization"; - PSO-Quantum, based on Sun, Xu, and Feng (2004) <doi:10.1109/ICCIS.2004.1460396>, "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization"; - PSO-Dexp, based on Stehlà k et al. (2024) <doi:10.1016/j.asoc.2024.111913>, "A Double Exponential Particle Swarm Optimization with Non-Uniform Variates as Stochastic Tuning and Guaranteed Convergence to a Global Optimum with Sample Applications to Finding Optimal Exact Designs in Biostatistics"; - and PSO-GO.

This package provides a framework to assist creation of marine ecosystem models, generating either R or C++ code which can then be optimised using the TMB package and standard R tools. Principally designed to reproduce gadget2 models in TMB', but can be extended beyond gadget2's capabilities. Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) <doi:10.18637/jss.v070.i05> "TMB: Automatic Differentiation and Laplace Approximation.". Begley, J., & Howell, D. (2004) <https://files01.core.ac.uk/download/pdf/225936648.pdf> "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.".

This package implements Bayesian parameter and Generalized Process Capability Indices (GPCIs) estimation using the Lindley approximation method (Lindley, 1980 <doi:10.2307/2345271>) under progressive Type-II censored data (Balakrishnan & Aggarwala, 2000 <doi:10.1007/978-1-4612-1334-5>). Evaluates point estimates and posterior expectations for classical and non-normal capability indices, including Cpy (Maiti et al., 2010 <doi:10.1080/16843703.2010.11673233>), Spmk (Dey & Saha, 2019 <doi:10.1007/s41872-019-00081-4>), CpTk (Saha et al., 2019 <doi:10.1007/s13198-019-00789-7>), Cpc (Saha et al., 2022 <doi:10.1080/02664763.2021.1971632>), CNpmc (Alotaibi et al., 2022 <doi:10.1155/2022/3135264>), CNpmkc (Saha et al., 2024 <doi:10.1142/S021853932450013X>), CNpk (Saha et al., 2018 <doi:10.1080/21681015.2018.1437793>), and Vannman's Cp(u,v) family (Vannman, 1995 <doi:10.1111/j.1467-9574.1995.tb01472.x>). Calculates point estimates, bias, mean squared error (MSE), Bayes risk under Linex and squared error loss, Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, and Heidelberger and Welch's MCMC convergence diagnostics (Heidelberger & Welch, 1983 <doi:10.1287/opre.31.6.1109>) with convergence probabilities. Accommodates user-defined probability density/mass functions, cumulative distribution functions, and survival functions. Supports progressive parametric and non-parametric bootstrap confidence intervals (Efron, 1987 <doi:10.1080/01621459.1987.10478410>) at 90%, 95%, and 99% significance levels.

Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.

Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) <doi:10.1080/01621459.2013.824892>. Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>. Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.

Simulating composite endpoints with recurrent and terminal events under staggered entry, and for constructing one- and two-sample group sequential test statistics and monitoring boundaries based on the mean frequency function. Details will be available in an upcoming publication.

Allows users to quickly and easily generate fake data containing Personally Identifiable Information (PII) through convenience functions.

This package provides extension types and conversions to between R-native object types and Arrow columnar types. This includes integration among the arrow', nanoarrow', sf', and wk packages such that spatial metadata is preserved wherever possible. Extension type implementations ensure first-class geometry data type support in the arrow and nanoarrow packages.

This package provides a procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.

Fits marginal regression models for repeated or clustered responses using generalized estimating equations (GEE). Provides ordinary GEE estimators, bias-reduced and bias-corrected GEE estimators, and Jeffreys-type penalized GEE estimators for binary, count and continuous responses. Methods are described in Touloumis (2026a) <doi:10.48550/arXiv.2606.16043> and Touloumis (2026b) <doi:10.48550/arXiv.2606.16058>.