This package implements the orthogonal reparameterization approach recommended by Lancaster (2002) to estimate dynamic panel models with fixed effects (and optionally: panel specific intercepts). The approach uses a likelihood-based estimator and produces estimates that are asymptotically unbiased as N goes to infinity, with a T as low as 2.

Quantifies hypothesis to data fit for repeated measures and longitudinal data, as described by Thorngate (1987) <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as pairwise relative orderings which are then compared to determine the percentage of orderings in the data that are matched by the hypothesis.

Classify Open Street Map (OSM) features into meaningful functional or analytical categories. Designed for OSM PBF files, e.g. from <https://download.geofabrik.de/> imported as spatial data frames. A classification consists of a list of categories that are related to certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main osm_classify() function returns a classification data table giving, for each feature, the primary and alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. The package also contains a classification of OSM features by economic function/significance, following Krantz (2023) <https://www.ssrn.com/abstract=4537867>.

Set of tools to generate samples of k-th order statistics and others quantities of interest from new families of distributions. The main references for this package are: C. Kleiber and S. Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag; Naradajah, S. and Rocha, R. (2016), <DOI:10.18637/jss.v069.i10> and Stasinopoulos, M. and Rigby, R. (2015), <DOI:10.1111/j.1467-9876.2005.00510.x>. The families of distributions are: Benini distributions, Burr distributions, Dagum distributions, Feller-Pareto distributions, Generalized Pareto distributions, Inverse Pareto distributions, The Inverse Paralogistic distributions, Marshall-Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distributions, Skew normal type 1 distributions, Skew student t distributions, Singh-Maddala distributions, Sinh-Arcsinh distributions, Sichel distributions, Zero inflated Poisson distributions.

This package implements the Bayesian online changepoint detection method by Adams and MacKay (2007) <arXiv:0710.3742> for univariate or multivariate data. Gaussian and Poisson probability models are implemented. Provides post-processing functions with alternative ways to extract changepoints.

This package provides an R interface to the OMOPHub API for accessing OHDSI ATHENA standardized medical vocabularies. Supports concept search, vocabulary exploration, hierarchy navigation, relationship queries, and concept mappings with automatic pagination and rate limiting.

Access data and processing functionalities of openEO compliant back-ends in R.

This package provides simple crosstab output with optional statistics (e.g., Goodman-Kruskal Gamma, Somers d, and Kendall's tau-b) as well as two-way and one-way tables. The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data.

DNA methylation is an important epigenetic process that regulates gene activity through chemical modifications of DNA without changing its sequence. OpEnCAST is a plant-specific ensemble-based prediction package that identifies 4mC, 5mC and 6mA methylation sites directly from DNA sequences. It combines multiple machine learning algorithms trained on monocot (Oryza sp.) and dicot (Arabidopsis sp.) reference models to deliver accurate predictions. This methodology is being inspired by the ensemble algorithm for methylation prediction developed by Wang et al. (2022) <doi:10.1186/s12859-022-04756-1>.

Extract results into R from the Observational Health Data Sciences and Informatics result database (see <https://ohdsi.github.io/Strategus/results-schema/index.html>) and generate reports/presentations via quarto that summarize results in HTML format. Learn more about OhdsiReportGenerator at <https://ohdsi.github.io/OhdsiReportGenerator/>.

This package infers Boolean rules among cis-regulatory regions using paired chromatin accessibility and gene expression data at bulk and single-cell levels. Links regulatory regions to target genes, providing insights into gene regulation mechanisms.

Sample from the limiting distributions of empirical Wasserstein distances under the null hypothesis and under the alternative. Perform a two-sample test on multivariate data using these limiting distributions and binning.

This package provides a set of tools to extract bibliographic content from OpenAlex database using API <https://docs.openalex.org>.

This package provides a DBI-compatible interface to ODBC databases.

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.

Ordnance Survey ('OS') is the national mapping agency for Great Britain and produces a large variety of mapping and geospatial products. Much of OS's data is available via the OS Data Hub <https://osdatahub.os.uk/>, a platform that hosts both free and premium data products. osdatahub provides a user-friendly way to access, query, and download these data.

An interface between R and the OSRM API. OSRM is a routing service based on OpenStreetMap data. See <http://project-osrm.org/> for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance).

This package implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as survey sliders, dose-response relationships and indexes. For more information, see Kubinec (2023) <doi:10.31235/osf.io/2sx6y>. The package is a front-end to the R package brms', which facilitates a range of regression specifications, including hierarchical, dynamic and multivariate modeling.

An implementation for computing Optimal B-Robust Estimators of two-parameter distribution. The procedure is composed of some equations that are evaluated alternatively until the solution is reached. Some tools for analyzing the estimates are included. The most relevant is covariance matrix computation using a closed formula.

Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.

Splits initial strata into refined strata that optimize covariate balance. For more information, please see Brumberg, Small, and Rosenbaum (2024) <doi:10.1093/biomtc/ujae061>. To solve the linear program, the Gurobi commercial optimization software is recommended, but not required. The gurobi R package can be installed following the instructions at <https://docs.gurobi.com/projects/optimizer/en/current/reference/r/setup.html> after claiming your free academic license at <https://www.gurobi.com/academia/academic-program-and-licenses/>.

Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) <doi:10.1080/00949655.2016.1238088>.

Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) <https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx>.

Uses the outputs of a logistic regression model, from caret <https://CRAN.R-project.org/package=caret>, to build an odds plot. This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model.