Allows you to connect to an Alfresco content management repository and interact with its contents using simple and intuitive functions. You will be able to establish a connection session to the Alfresco repository, read and upload content and manage folder hierarchies. For more details on the Alfresco content management repository see <https://www.alfresco.com/ecm-software/document-management>.

Adaptive Gauss Hermite Quadrature for Bayesian inference. The AGHQ method for normalizing posterior distributions and making Bayesian inferences based on them. Functions are provided for doing quadrature and marginal Laplace approximations, and summary methods are provided for making inferences based on the results. See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package" <arXiv:2101.04468>.

This package performs logistic regression for binary longitudinal data, allowing for serial dependence among observations from a given individual and a random intercept term. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed, with some restrictions. M. Helena Goncalves et al.(2007) <DOI: 10.18637/jss.v046.i09>.

CEU (CEU San Pablo University) Mass Mediator is an on-line tool for aiding researchers in performing metabolite annotation. cmmr (CEU Mass Mediator RESTful API) allows for programmatic access in R: batch search, batch advanced search, MS/MS (tandem mass spectrometry) search, etc. For more information about the API Endpoint please go to <https://github.com/YaoxiangLi/cmmr>.

Connect and pull data from the CJA API, which powers CJA Workspace <https://github.com/AdobeDocs/cja-apis>. The package was developed with the analyst in mind and will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. New features are actively being developed and we value your feedback and contribution to the process.

This package performs causal functional mediation analysis (CFMA) for functional treatment, functional mediator, and functional outcome. This package includes two functional mediation model types: (1) a concurrent mediation model and (2) a historical influence mediation model. See Zhao et al. (2018), Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data, <arXiv:1805.06923> for details.

This package provides functions for handling missing data using Distributed Trimmed Scores Regression and other imputation methods. It includes facilities for data imputation, evaluation metrics, and clustering analysis. It is designed to work in distributed computing environments to handle large datasets efficiently. The philosophy of the package is described in Guo G. (2024) <doi:10.1080/03610918.2022.2091779>.

Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. Yu and Rosenbaum (2019) <doi:10.1111/biom.13098>.

Fits Leroux model in spectral domain to estimate causal spatial effect as detailed in Guan, Y; Page, G.L.; Reich, B.J.; Ventrucci, M.; Yang, S; (2020) <arXiv:2012.11767>. Both the parametric and semi-parametric models are available. The semi-parametric model relies on INLA'. The INLA package can be obtained from <https://www.r-inla.org/>.

This package provides functions for the calculation of greenhouse gas flux rates from closed chamber concentration measurements. The package follows a modular concept: Fluxes can be calculated in just two simple steps or in several steps if more control in details is wanted. Additionally plot and preparation functions as well as functions for modelling gpp and reco are provided.

This package implements the Mode Jumping Markov Chain Monte Carlo algorithm described in <doi:10.1016/j.csda.2018.05.020> and its Genetically Modified counterpart described in <doi:10.1613/jair.1.13047> as well as the sub-sampling versions described in <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.

This package provides a Gibbs sampler corresponding to a Group Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates. Hyperparameters in the GIGG prior specification can either be fixed by the user or can be estimated via Marginal Maximum Likelihood Estimation. Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar Mukherjee (2021) <arXiv:2102.10670>.

This package implements hierarchically regularized entropy balancing proposed by Xu and Yang (2022) <doi:10.1017/pan.2022.12>. The method adjusts the covariate distributions of the control group to match those of the treatment group. hbal automatically expands the covariate space to include higher order terms and uses cross-validation to select variable penalties for the balancing conditions.

An interactive presentation on the topic of Multinomial Logistic Regression. It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at <https://jarvisatharva.shinyapps.io/MultinomPresentation>.

An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008) <doi:10.1287/opre.1070.0496>, Heinrich (1998) <doi:10.1006/jcom.1998.0471>, for R. This package builds on the original Matlab and C++ implementations by Mike Giles to provide a full MLMC driver and example level samplers. Multi-core parallel sampling of levels is provided built-in.

Crawler for OJS pages and scraper for meta-data from articles. You can crawl OJS archives, issues, articles, galleys, and search results. You can scrape articles metadata from their head tag in html, or from Open Archives Initiative ('OAI') records. Most of these functions rely on OJS routing conventions (<https://docs.pkp.sfu.ca/dev/documentation/en/architecture-routes>).

This package provides classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996) <doi:10.1111/j.1467-9892.1996.tb00281.x>.

For a data matrix with m rows and n columns (m>=n), the power method is used to compute, simultaneously, the eigendecomposition of a square symmetric matrix. This result is used to obtain the singular value decomposition (SVD) and the principal component analysis (PCA) results. Compared to the classical SVD method, the first r singular values can be computed.

This package implements estimation methods for shrinkage covariance matrices using user-specified covariance targets. The covariance target is a structured matrix towards which the unbiased sample covariance is shrunk, optionally incorporating prior knowledge. Shrinkage intensity is computed analytically. The method is described and applied to microarray gene expression data in Jelizarow et al. (2010) <doi:10.1093/bioinformatics/btq323>.

Application of theoretical results which ensure that the summation of an infinite discrete series is within an arbitrary margin of error of its true value. The C code under the hood is shared through header files to allow users to sum their own low level functions as well. Based on the paper by Braden (1992) <doi: 10.2307/2324995>.

Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.

Fits singular linear models to longitudinal data. Singular linear models are useful when the number, or timing, of longitudinal observations may be informative about the observations themselves. They are described in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general longitudinal data.

An R wrapper around the API of TheyWorkForYou, a parliamentary monitoring site that scrapes and repackages Hansard (the UK's parliamentary record) and augments it with information from the Register of Members Interests, election results, and voting records to provide a unified source of information about UK legislators and their activities. See <http://www.theyworkforyou.com> for details.

Analyze data from behavioral experiments conducted using MED-PC software developed by Med Associates Inc. Includes functions to fit exponential and hyperbolic models for delay discounting tasks, exponential mixtures for inter-response times, and Gaussian plus ramp models for peak procedure data, among others. For more details, refer to Alcala et al. (2023) <doi:10.31234/osf.io/8aq2j>.