This package implements higher order likelihood-based inference for logistic and loglinear models.

Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps, following Audigier and Niang 2022 <doi:10.1007/s11634-022-00519-1>. I) Missing data imputation using dedicated models. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods, as discussed in Audigier et al. (2021) <doi:10.48550/arXiv.2106.04424>. II) cluster analysis of imputed data sets. Six clustering methods are available (distances-based or model-based), but custom methods can also be easily used. III) Partition pooling. The set of partitions is aggregated using Non-negative Matrix Factorization based method. An associated instability measure is computed by bootstrap (see Fang, Y. and Wang, J., 2012 <doi:10.1016/j.csda.2011.09.003>). Among applications, this instability measure can be used to choose a number of clusters with missing values. The package also proposes several diagnostic tools to tune the number of imputed data sets, to tune the number of iterations in fully sequential imputation, to check the fit of imputation models, etc.

Calculate the distance between single-arm observational studies using covariate information to remove heterogeneity in Network Meta-Analysis (NMA) of randomized clinical trials. Facilitate the inclusion of observational data in NMA, enhancing the comprehensiveness and robustness of comparative effectiveness research. Schmitz (2018) <doi:10.1186/s12874-018-0509-7>.

The bivariate copula mixed model for meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos (2015) <doi:10.1002/sim.6595> and Nikoloulopoulos (2018) <doi:10.1007/s10182-017-0299-y>. The vine copula mixed model for meta-analysis of diagnostic test accuracy studies accounting for disease prevalence in Nikoloulopoulos (2017) <doi:10.1177/0962280215596769> and also accounting for non-evaluable subjects in Nikoloulopoulos (2020) <doi:10.1515/ijb-2019-0107>. The hybrid vine copula mixed model for meta-analysis of diagnostic test accuracy case-control and cohort studies in Nikoloulopoulos (2018) <doi:10.1177/0962280216682376>. The D-vine copula mixed model for meta-analysis and comparison of two diagnostic tests in Nikoloulopoulos (2019) <doi:10.1177/0962280218796685>. The multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic tests with non-evaluable subjects in Nikoloulopoulos (2020) <doi:10.1177/0962280220913898>. The one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests in Nikoloulopoulos (2022) <doi:10.1111/rssa.12838>. The multinomial six-variate 1-truncated D-vine copula mixed model for meta-analysis of two diagnostic tests accounting for within and between studies dependence in Nikoloulopoulos (2024) <doi:10.1177/09622802241269645>. The 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard (Nikoloulopoulos, 2025) <doi:10.1093/biomtc/ujaf037>.

This package provides the official administrative boundaries of the Azores (Região Autónoma dos Açores (RAA)) as defined in the 2024 edition of the Carta Administrativa Oficial de Portugal (CAOP), published by the Direção-Geral do Território (DGT). The package includes convenience functions to import these boundaries as sf objects for spatial analysis in R. Source: <https://geo2.dgterritorio.gov.pt/caop/CAOP_RAA_2024-gpkg.zip>.

Fits a spatio-temporal finite mixture model using TMB'. Covariate, spatial and temporal random effects can be incorporated into the gating formula using multinomial logistic regression, the expert formula using a generalized linear mixed model framework, or both.

This package provides functions for fitting univariate linear regression models under Scale Mixtures of Skew-Normal (SMSN) distributions, considering left, right or interval censoring and missing responses. Estimation is performed via an EM-type algorithm. Includes selection criteria, sample generation and envelope. For details, see Gil, Y.A., Garay, A.M., and Lachos, V.H. (2025) <doi:10.1007/s10260-025-00797-x>.

Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).

This package provides a constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019)<doi:10.18637/jss.v089.i05> for more details.

Use machine learning algorithms and advanced geographic information system tools to build Species Distribution Modeling in a extensible and modern fashion.

Compare two classifications or clustering solutions that may or may not have the same number of classes, and that might have hard or soft (fuzzy, probabilistic) membership. Calculate various metrics to assess how the clusters compare to each other. The calculations are simple, but provide a handy tool for users unfamiliar with matrix multiplication. This package is not geared towards traditional accuracy assessment for classification/ mapping applications - the motivating use case is for comparing a probabilistic clustering solution to a set of reference or existing class labels that could have any number of classes (that is, without having to degrade the probabilistic clustering to hard classes).

Defines classes and methods to cross-validate various binary classification algorithms used for "class prediction" problems.

This package provides a collection of functions to generate a large variety of structures in high dimensions. These data structures are useful for testing, validating, and improving algorithms used in dimensionality reduction, clustering, machine learning, and visualization.

Collection of utility functions for visualizing body map data collected with the Collaborative Health Outcomes Information Registry.

This package provides an expectation maximization (EM) algorithm to fit a mixture of continuous time Markov models for use with clickstream or other sequence type data. Gallaugher, M.P.B and McNicholas, P.D. (2018) <arXiv:1802.04849>.

CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.

This package provides the basic functionality to interact with the Collatz conjecture. The parameterisation uses the same (P,a,b) notation as Conway's generalisations. Besides the function and reverse function, there is also functionality to retrieve the hailstone sequence, the "stopping time"/"total stopping time", or tree-graph. The only restriction placed on parameters is that both P and a can't be 0. For further reading, see <https://en.wikipedia.org/wiki/Collatz_conjecture>.

Applies the change-in-effect estimate method to assess confounding effects in medical and epidemiological research (Greenland & Pearce (2016) <doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model including only the outcome and exposure variables. At each of the subsequent steps, one variable which creates the largest change among the remaining variables is selected. This process is repeated until all variables have been entered into the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183â 196). Currently, the chest package has functions for linear regression, logistic regression, negative binomial regression, Cox proportional hazards model and conditional logistic regression.

This package creates auto-grading check-fields and check-boxes for rmarkdown or quarto HTML. It can be used in class, when teacher share materials and tasks, so students can solve some problems and check their work. In contrast to the learnr package, the checkdown package works serverlessly without shiny'.

This package provides a simple interface to pull County Health Rankings & Roadmaps (CHR&R) county-level health data and metadata directly from Zenodo <doi:10.5281/zenodo.18157681>. Users can retrieve data for CHR&R release years 2010 through 2025. CHR&R data support research and decision-making to promote health equity and policies that help all communities thrive.

Calculation of standard deviation scores and percentiles adduced from different standards (WHO, UK, Germany, Italy, China, etc). Also, references for laboratory values in children and adults are available, e.g., serum lipids, iron-related blood parameters, IGF, liver enzymes. See package documentation for full list.

This package provides a comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.

Synthesizing joint distributions from marginal densities, focusing on controlling key statistical properties such as correlation for continuous data, mutual information for categorical data, and inducing Simpson's Paradox. Generate datasets with specified correlation structures for continuous variables, adjust mutual information between categorical variables, and manipulate subgroup correlations to intentionally create Simpson's Paradox. Joe (1997) <doi:10.1201/b13150> Sklar (1959) <https://en.wikipedia.org/wiki/Sklar%27s_theorem>.

Under natural conditions, nest temperatures fluctuate daily around a mean value, whereas in captivity they are often held constant. The Constant Temperature Equivalent is designed to bridge the gap between the two by calculating a single temperature value for wild nests that corresponds with the amount of development that would occur in an incubator set to the same temperature. The theory and formulas behind this method were developed by Professor Author Georges and are implemented here as a single function.