Run computer experiments using the adaptive composite grid algorithm with a Gaussian process model. The algorithm works best when running an experiment that can evaluate thousands of points from a deterministic computer simulation. This package is an implementation of a forthcoming paper by Plumlee, Erickson, Ankenman, et al. For a preprint of the paper, contact the maintainer of this package.

Provide functions for overlaps clustering, fuzzy clustering and interval-valued data manipulation. The package implement the following algorithms: OKM (Overlapping Kmeans) from Cleuziou, G. (2007) <doi:10.1109/icpr.2008.4761079> ; NEOKM (Non-exhaustive overlapping Kmeans) from Whang, J. J., Dhillon, I. S., and Gleich, D. F. (2015) <doi:10.1137/1.9781611974010.105> ; Fuzzy Cmeans from Bezdek, J. C. (1981) <doi:10.1007/978-1-4757-0450-1> ; Fuzzy I-Cmeans from de A.T. De Carvalho, F. (2005) <doi:10.1016/j.patrec.2006.08.014>.

Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.

Playfair, Four-Square, Scytale, Columnar Transposition and Autokey methods. Further explanation on methods of classical cryptography can be found at Wikipedia; (<https://en.wikipedia.org/wiki/Classical_cipher>).

Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.

Estimation of Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) <doi:10.1111/1467-9965.00114> as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) <doi:10.1111/j.1467-9868.2005.00508.x> are included.

Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.

This package implements algorithms for analyzing Cayley graphs of permutation groups, with a focus on the TopSpin puzzle and similar permutation-based combinatorial puzzles. Provides methods for cycle detection, state space exploration, bidirectional BFS pathfinding, and finding optimal operation sequences in permutation groups generated by shift and reverse operations. Includes C++ implementations of core operations via Rcpp for performance. Optional GPU acceleration via ggmlR Vulkan backend for batch distance calculations and parallel state transformations.

Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. Estimation of copula parameter(COPC) and Conditional quantile estimation are included for five available copula functions. Copula selection methods based on L2 distance from empirical copula function are also included.

Integrated, convenient, and uniform access to Canadian Census data and geography retrieved using the CensusMapper API. This package produces analysis-ready tidy data frames and spatial data in multiple formats, as well as convenience functions for working with Census variables, variable hierarchies, and region selection. API keys are freely available with free registration at <https://censusmapper.ca/api>. Census data and boundary geometries are reproduced and distributed on an "as is" basis with the permission of Statistics Canada (Statistics Canada 1996; 2001; 2006; 2011; 2016; 2021).

Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data.

Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martà et al (2018, ISBN:978-84-09-07541-6), Creus-Martà et al (2021) <doi:10.1155/2021/9951817> and Creus-Martà et al (2022) <doi:10.1155/2022/4907527>.

Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package ChannelAttribution'.

This package provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal <https://portaldatransparencia.gov.br/download-de-dados/cpgf/> and a curated dataset hosted on the Open Science Framework <https://osf.io/z2mxc/>. Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism.

Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" <doi:10.1093/biostatistics/kxac023>.

Single objective optimization using a CMA-ES.

This package provides tools for Delphi's COVIDcast Epidata API: data access, maps and time series plotting, and basic signal processing. The API includes a collection of numerous indicators relevant to the COVID-19 pandemic in the United States, including official reports, de-identified aggregated medical claims data, large-scale surveys of symptoms and public behavior, and mobility data, typically updated daily and at the county level. All data sources are documented at <https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html>.

Correlates of protection (CoP) and correlates of risk (CoR) study the immune biomarkers associated with an infectious disease outcome, e.g. COVID or HIV-1 infection. This package contains shared functions for analyzing CoP and CoR, including bootstrapping procedures, competing risk estimation, and bootstrapping marginalized risks.

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>.

Doubly robust methods for evaluating surrogate markers as outlined in: Agniel D, Hejblum BP, Thiebaut R & Parast L (2022). "Doubly robust evaluation of high-dimensional surrogate markers", Biostatistics <doi:10.1093/biostatistics/kxac020>. You can use these methods to determine how much of the overall treatment effect is explained by a (possibly high-dimensional) set of surrogate markers.

It helps in development of a principal component analysis based composite index by assigning weights to variables and combining the weighted variables. For method details see Sendhil, R., Jha, A., Kumar, A. and Singh, S. (2018). <doi:10.1016/j.ecolind.2018.02.053>, and Wu, T. (2021). <doi:10.1016/j.ecolind.2021.108006>.

In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various statistics are computed. This package can also be used for analyzing the results from random experiments.

This package provides a set of utility tools to inspect spatial objects, facilitate handling and reporting of topology errors and geometry validity issue with sp objects. Finally, it provides a geometry cleaner that will fix all geometry problems, and eliminate (at least reduce) the likelihood of having issues when doing spatial data processing.

The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review).