This package contains functions for estimating generalized parametric mixture and non-mixture cure models <doi:10.1016/j.cmpb.2022.107125>, loss of lifetime, mean residual lifetime, and crude event probabilities.

Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis.

Given a patient-sharing network, calculate either the classic care density as proposed by Pollack et al. (2013) <doi:10.1007/s11606-012-2104-7> or the fragmented care density as proposed by Engels et al. (2024) <doi:10.1186/s12874-023-02106-0>. By utilizing the igraph and data.table packages, the provided functions scale well for very large graphs.

The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.

This package provides a tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting. Users can either run the pipeline as a whole, or choose to run any one of the three individual steps. It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models.

Color palettes for EPL, MLB, NBA, NHL, and NFL teams.

Implementation of a probabilistic method for biclustering adapted to overdispersed count data. It is a Gamma-Poisson Latent Block Model. It also implements two selection criteria in order to select the number of biclusters.

Dissects a package environment or covr coverage object in order to cross reference tested code with the lines that are evaluated, as well as linking those evaluated lines to the documentation that they are described within. Connecting these three pieces of information provides a mechanism of linking tests to documented behaviors.

This package performs the cross-match test that is an exact, distribution free test of equality of 2 high dimensional multivariate distributions. The input is a distance matrix and the labels of the two groups to be compared, the output is the number of cross-matches and a p-value. See Rosenbaum (2005) <doi:10.1111/j.1467-9868.2005.00513.x>.

This package provides function to create, read, write, and work with iCalendar files (which typically have .ics or .ical extensions), and the scheduling data, calendars and timelines of people, organisations and other entities that they represent. iCalendar is an open standard for exchanging calendar and scheduling information between users and computers, described at <https://icalendar.org/>.

Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.

This package provides methods for learning causal relationships among a set of foreground variables X based on signals from a (potentially much larger) set of background variables Z, which are known non-descendants of X. The confounder blanket learner (CBL) uses sparse regression techniques to simultaneously perform many conditional independence tests, with complementary pairs stability selection to guarantee finite sample error control. CBL is sound and complete with respect to a so-called "lazy oracle", and works with both linear and nonlinear systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.

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>.

This package provides a new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.

Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.

Package to analyze the clinical utility of a biomarker. It provides the clinical utility curve, clinical utility table, efficacy of a biomarker, clinical efficacy curve and tests to compare efficacy between markers.

Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.

This package provides functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.

Copula-based imputation methods: parametric and nonparametric algorithms for missing multivariate data through conditional copulas.

This package provides functions to work with data frames to prepare data for further analysis. The functions for imputation, encoding, partitioning, and other manipulation can produce log files to keep track of process.

This package provides functions supporting the common needs of packages ChemoSpec and ChemoSpec2D'.

This package provides a collection of user-submitted functions to aid in the analysis of hydrological data, particularly for users in Canada. The functions focus on the use of Canadian data sets, and are suited to Canadian hydrology, such as the important cold region hydrological processes and will work with Canadian hydrological models. The functions are grouped into several themes, currently including Statistical hydrology, Basic data manipulations, Visualization, and Spatial hydrology. Functions developed by the Floodnet project are also included. CSHShydRology has been developed with the assistance of the Canadian Society for Hydrological Sciences (CSHS) which is an affiliated society of the Canadian Water Resources Association (CWRA). As of version 1.2.6, functions now fail gracefully when attempting to download data from a url which is unavailable.

This package provides tools for detecting cellwise outliers and robust methods to analyze data which may contain them. Contains the implementation of the algorithms described in Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access) Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access), Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access), Raymaekers and Rousseeuw (2022) <doi:10.1080/01621459.2023.2267777> (open access) Rousseeuw (2022) <doi:10.1016/j.ecosta.2023.01.007> (open access). Examples can be found in the vignettes: "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples", "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples", and "cellwise_weights_examples".

CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.