Calculating the fractal dimension of a coastline using the boxes and dividers methods.

Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling and simulation with powerful tools for Nonlinear Mixed-Effects (NLME) modeling. The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data, and support integrated use within the Pirana pharmacometric workbench <doi:10.1002/psp4.70067>. Execution is supported both locally or on remote machines.

This package provides a toolkit for querying Team Cymru <http://team-cymru.org> IP address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon and Malware Hash Data Services.

Method to implement some newly developed methods for the estimation of the conditional survival function. See Meira-Machado, Sestelo and Goncalves (2016) <doi:10.1002/bimj.201500038>.

This package provides a set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individualsâ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.

This package provides a data package with 2 main package variables: signature and etiology'. The signature variable contains the latest mutational signature profiles released on COSMIC <https://cancer.sanger.ac.uk/signatures/> for 3 mutation types: * Single base substitutions in the context of preceding and following bases, * Doublet base substitutions, and * Small insertions and deletions. The etiology variable provides the known or hypothesized causes of signatures. cosmicsig stands for COSMIC signatures. Please run ?'cosmicsig for more information.

This package provides a graphical user interface for simulating the effects of mergers, tariffs, and quotas under an assortment of different economic models. The interface is powered by the Shiny web application framework from RStudio'.

Incorporates colour gradients from the cpt-city web archive available at <http://seaviewsensing.com/pub/cpt-city/>.

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.

Constrained ordinary least squares is performed. One constraint is that all beta coefficients (including the constant) cannot be negative. They can be either 0 or strictly positive. Another constraint is that the sum of the beta coefficients equals a constant. References: Hansen, B. E. (2022). Econometrics, Princeton University Press. <ISBN:9780691235899>.

Helping biologists to choose the most suitable approach to link their research to conservation. After answering few questions on the data available, geographic and taxonomic scope, conserveR ranks existing methods for conservation prioritization and systematic conservation planning by suitability. The methods data base of conserveR contains 133 methods for conservation prioritization based on a systematic review of > 12,000 scientific publications from the fields of spatial conservation prioritization, systematic conservation planning, biogeography and ecology.

Solves multivariate least squares (MLS) problems subject to constraints on the coefficients, e.g., non-negativity, orthogonality, equality, inequality, monotonicity, unimodality, smoothness, etc. Includes flexible functions for solving MLS problems subject to user-specified equality and/or inequality constraints, as well as a wrapper function that implements 24 common constraint options. Also does k-fold or generalized cross-validation to tune constraint options for MLS problems. See ten Berge (1993, ISBN:9789066950832) for an overview of MLS problems, and see Goldfarb and Idnani (1983) <doi:10.1007/BF02591962> for a discussion of the underlying quadratic programming algorithm.

This package provides a user friendly function crrcbcv to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) <doi:10.1111/j.0006-341X.2001.00126.x>, the KC-type bias correction by Kauermann and Carroll (2001) <doi:10.1198/016214501753382309>, the FG-type bias correction by Fay and Graubard (2001) <doi:10.1111/j.0006-341X.2001.01198.x>, and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) <doi:10.1002/bimj.200390021>.

Offers a set of objects tailored to simplify working with choice data. It enables the computation of choice probabilities and the likelihood of various types of choice models based on given data.

The biases introduced in association measures, particularly mutual information, are influenced by factors such as tumor purity, mutation burden, and hypermethylation. This package provides the estimation of conditional mutual information (CMI) and its statistical significance with a focus on its application to multi-omics data. Utilizing B-spline functions (inspired by Daub et al. (2004) <doi:10.1186/1471-2105-5-118>), the package offers tools to estimate the association between heterogeneous multi- omics data, while removing the effects of confounding factors. This helps to unravel complex biological interactions. In addition, it includes methods to evaluate the statistical significance of these associations, providing a robust framework for multi-omics data integration and analysis. This package is ideal for researchers in computational biology, bioinformatics, and systems biology seeking a comprehensive tool for understanding interdependencies in omics data.

Estimates hidden Markov models from the family of Cholesky-decomposed Gaussian hidden Markov models (CDGHMM) under various missingness schemes. This family improves upon estimation of traditional Gaussian HMMs by introducing parsimony, as well as, controlling for dropped out observations and non-random missingness. See Neal, Sochaniwsky and McNicholas (2024) <DOI:10.1007/s11222-024-10462-0>.

Assess the calibration of an existing (i.e. previously developed) multistate model through calibration plots. Calibration is assessed using one of three methods. 1) Calibration methods for binary logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 2) Calibration methods for multinomial logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 3) Pseudo-values estimated using the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction with landmarking when required. These calibration plots evaluate the calibration (in a validation cohort of interest) of the transition probabilities estimated from an existing multistate model. While package development has focused on multistate models, calibration plots can be produced for any model which utilises information post baseline to update predictions (e.g. dynamic models); competing risks models; or standard single outcome survival models, where predictions can be made at any landmark time. Please see Pate et al. (2024) <doi:10.1002/sim.10094> and Pate et al. (2024) <https://alexpate30.github.io/calibmsm/articles/Overview.html>.

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'.

Additive copula regression for regression problems with binary outcome via gradient boosting [Brant, Hobæk Haff (2022); <arXiv:2208.04669>]. The fitting process includes a specialised model selection algorithm for each component, where each component is found (by greedy optimisation) among all the D-vines with only Gaussian pair-copulas of a fixed dimension, as specified by the user. When the variables and structure have been selected, the algorithm then re-fits the component where the pair-copula distributions can be different from Gaussian, if specified.

The cmgnd implements the constrained mixture of generalized normal distributions model, a flexible statistical framework for modelling univariate data exhibiting non-normal features such as skewness, multi-modality, and heavy tails. By imposing constraints on model parameters, the cmgnd reduces estimation complexity while maintaining high descriptive power, offering an efficient solution in the presence of distributional irregularities. For more details see Duttilo and Gattone (2025) <doi:10.1007/s00180-025-01638-x> and Duttilo et al (2025) <doi:10.48550/arXiv.2506.03285>.

OpenAI's ChatGPT <https://chat.openai.com/> coding assistant for RStudio'. A set of functions and RStudio addins that aim to help the R developer in tedious coding tasks.

This package implements a classification method described by Grice (2011, ISBN:978-0-12-385194-9) using binary procrustes rotation; a simplified version of procrustes rotation.

This package performs analysis of complex dynamic systems with a focus on the temporal unfolding of patterns, changes, and state transitions in behavioral data. Supports both time series and sequence data and provides tools for the analysis and visualization of complexity, pattern identification, trends, regimes, sequence typology as well as early warning signals.

Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. An R wrapper is included but this package is primarily designed to be used from C code using LinkingTo'. The spline calculations are classical cubic interpolation, e.g., Forsythe, Malcolm and Moler (1977) <ISBN: 9780131653320>.