This package provides functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of clusteredness in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2018) <doi:10.1080/10618600.2017.1349664>. We provide an R native version with methods for printing, summarizing, and plotting the result.

An R interface to Cheetah Grid', a high-performance JavaScript table widget. cheetahR allows users to render millions of rows in just a few milliseconds, making it an excellent alternative to other R table widgets. The package wraps the Cheetah Grid JavaScript functions and makes them readily available for R users. The underlying grid implementation is based on Cheetah Grid <https://github.com/future-architect/cheetah-grid>.

This package provides tools for distribution-aware and city-adaptive analysis of learning-based road-network distance estimates. Provides detour-factor diagnostics, a Topological Predictability Index, upper-tail summaries, modular robust losses, hybrid objective components, adaptive validation weights, and multi-criteria model evaluation. The functions are designed as reusable building blocks rather than a fixed model specification.

There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the article titled New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs published in Scientific Reports. Please see the reference: Yu et al. (2019) <doi:10.1038/s41598-019-40167-8>.

Allows you to conduct robust correlations on your non-normal data set. The robust correlations included in the package are median-absolute-deviation and median-based correlations. Li, J.C.H. (2022) <doi:10.5964/meth.8467>.

Allows users to seamlessly query several CDC PLACES APIs (<https://data.cdc.gov/browse?q=PLACES%20&sortBy=relevance>) by geography, state, measure, and release year. This package also contains a function to explore the available measures for each release year.

Encrypts and decrypts strings using either the Caesar cipher or a pseudorandom number generation (using set.seed()) method.

This package provides tools to easily access and analyze Canadian Election Study data. The package simplifies the process of downloading, cleaning, and using CES datasets for political science research and analysis. The Canadian Election Study ('CES') has been conducted during federal elections since 1965, surveying Canadians on their political preferences, engagement, and demographics. Data is accessed from multiple sources including the Borealis Data repository <https://borealisdata.ca/> and the official Canadian Election Study website <https://ces-eec.arts.ubc.ca/>. This package is not officially affiliated with the Canadian Election Study, Borealis Data, or the University of British Columbia, and users should cite the original data sources in their work.

Generate synthetic station-based monthly climate time-series including temperature and rainfall, export to Network Common Data Form (NetCDF), and provide visualization helpers for climate workflows. The approach is inspired by statistical weather generator concepts described in Wilks (1999) <doi:10.1016/S0168-1923(99)00037-4> and Richardson (1981) <doi:10.1029/WR017i001p00182>.

Generalized computational algorithms for competing risks analysis, stress-strength reliability modeling, optimal designs, and reliability acceptance sampling plans under various hybrid censoring schemes. Includes data generation routines, Maximum Likelihood Estimation (MLE) with seven optimization algorithms ('Newton-Raphson', BFGS', BFGSR', BHHH', SANN', CG', and Nelder-Mead'), Bayesian inference via Gibbs sampling and Metropolis-Hastings MCMC, Importance Sampling, and Lindley asymptotic approximation. Visualization functions generate histograms, dot plots, and autocorrelation plots for model validation. Methodology and design principles are based on Balakrishnan', Cramer', and Kundu (2023, "Hybrid Censoring Know-How: Designs and Implementations", Academic Press, ISBN:978-0-12-398387-9).

Causal Inference Assistance (CIA) for performing causal inference within the structural causal modelling framework. Structure learning is performed using partition Markov chain Monte Carlo (Kuipers & Moffa, 2017) and several additional functions have been added to help with causal inference. Kuipers and Moffa (2017) <doi:10.1080/01621459.2015.1133426>.

Shiny app for creating interactive consort flow diagrams and other types of flow diagrams, see Moher, Schulz and Altman (2001) <doi:10.1016/S0140-6736(00)04337-3>.

Links polymorphic Observational Medical Outcomes Partnership (OMOP) COST records to clinical events and tracks medical expenditures, unit cost tariffs, and healthcare price indices for OMOP Common Data Model (CDM) cohorts following DARWIN EU standards.

This package implements cross-validation methods for linear and ridge regression models. The package provides grid-based selection of the ridge penalty parameter using Singular Value Decomposition (SVD) and supports K-fold cross-validation, Leave-One-Out Cross-Validation (LOOCV), and Generalized Cross-Validation (GCV). Computations are implemented in C++ via RcppArmadillo with optional parallelization using RcppParallel'. The methods are suitable for high-dimensional settings where the number of predictors exceeds the number of observations.

Computation of Multiscale Codependence Analysis and spatial eigenvector maps.

This package provides a complete toolkit for getting country data onto honest maps. Country names rarely line up across data sources ("US", "U.S.", "United States", "United States of America" are one country, but a naive join treats them as four), so countryatlas makes ISO codes the universal join key. It generalises a one-call, map-ready table that stitches together ggplot2 map geometry, WDI World Bank indicators and the countrycode Rosetta stone; exposes the join machinery for the user's own data; ships curated reference data (metadata, group memberships, an indicator catalogue, flags and currencies); adds analysis helpers (per-capita, regional roll-ups, ranking, inequality and convergence statistics); and turns one hand-drawn choropleth into a full vocabulary of projected, area-honest maps (binned and quantile choropleths, proportional-symbol, spike, bivariate, value-by-alpha, cartogram, tile-grid, flow, small-multiple, animated, globe and interactive), and can hand its curated, ISO-reconciled tables to ggsql for database-side spatial rendering. Honesty is treated as a feature rather than a slogan: classification methods can be compared side by side, missing data can be hatched rather than greyed, coverage and provenance travel with the plot, and the distortion each projection introduces can be measured and drawn. Heavy spatial dependencies stay optional, and a bundled offline snapshot lets every example, test and vignette run without the network.

Corbae-Ouliaris frequency domain filtering. According to Corbae and Ouliaris (2006) <doi:10.1017/CBO9781139164863.008>, this is a solution for extracting cycles from time series, like business cycles etc. when filtering. This method is valid for both stationary and non-stationary time series.

Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a shiny interface. The Pharmacometric Modeling Language (PML) code updates in real time given changes to user inputs. Models can be executed using the Certara.RsNLME package. Additional support to generate the underlying Certara.RsNLME code to recreate the corresponding model in R is provided in the user interface.

An implementation of methods for causal discovery in a structural causal model where the conditional distribution of the target node is described by a generalized linear model conditional on its causal parents.

This package implements the count splitting methodology from Neufeld et al. (2022) <doi:10.1093/biostatistics/kxac047> and Neufeld et al. (2023) <arXiv:2307.12985>. Intended for turning a matrix of single-cell RNA sequencing counts, or similar count datasets, into independent folds that can be used for training/testing or cross validation. Assumes that the entries in the matrix are from a Poisson or a negative binomial distribution.

Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2023) <doi: 10.18637/jss.v105.i01>, Augugliaro et al. (2020b) <doi: 10.1007/s11222-020-09945-7>, Augugliaro et al. (2020a) <doi: 10.1093/biostatistics/kxy043>, Yin et al. (2001) <doi: 10.1214/11-AOAS494> and Stadler et al. (2012) <doi: 10.1007/s11222-010-9219-7>.

Optimization solver based on the Cross-Entropy method.

Design and use of control charts for detecting mean changes based on a delayed updating of the in-control parameter estimates. See Capizzi and Masarotto (2019) <doi:10.1080/00224065.2019.1640096> for the description of the method.

This package implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, <doi:10.1007/BF00116466>) and Leroux model (Leroux et al., 2000, <doi:10.1007/978-1-4612-1284-3_4>). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.