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.

Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.

Gain access to the Spark Catalog API making use of the sparklyr API. Catalog <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).

Fast and memory-efficient (or cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide cheaper alternatives to common base R functions, as well as some additional functions.

Measuring child development starts by collecting responses to developmental milestones, such as "able to sit" or "says two words". There are many ways to combine such responses into summaries. The package bundles publicly available datasets with individual milestone data for children aged 0-5 years, with the aim of supporting the construction, evaluation, validation and interpretation of methodologies that aggregate milestone data into informative measures of child development.

Constrained quantile regression 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 beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. <doi:10.1017/CBO9780511754098>.

Implementation of the Control Polygon Reduction and Control Net Reduction methods for finding parsimonious B-spline regression models.

Converts any word, sentence or speech into Trump's infamous "covfefe" format. Reference: <https://www.nytimes.com/2017/05/31/us/politics/covfefe-trump-twitter.html>. Inspiration thanks to: <https://codegolf.stackexchange.com/questions/123685/covfefify-a-string>.

This package provides a big data version for fitting cumulative probability models using the orm() function from the rms package. See Liu et al. (2017) <DOI:10.1002/sim.7433> for details.

Confirms if the number is Luhn compliant. Can check if credit card, IMEI number or any other Luhn based number is correct. For more info see: <https://en.wikipedia.org/wiki/Luhn_algorithm>.

This package contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. CASMI is a fundamental concept of a series of methods. For more information about CASMI and CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author for the latest updates.

It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. It generates individual insurance claims including open claims, reopened claims, incurred but not reported claims and future claims. It also includes claim data fitting functions to help set simulation assumptions. It is useful for claim level reserving analysis. Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.

In the context of high-throughput genetic data, CoDaCoRe identifies a set of sparse biomarkers that are predictive of a response variable of interest (Gordon-Rodriguez et al., 2021) <doi:10.1093/bioinformatics/btab645>. More generally, CoDaCoRe can be applied to any regression problem where the independent variable is Compositional (CoDa), to derive a set of scale-invariant log-ratios (ILR or SLR) that are maximally associated to a dependent variable.

It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.

This package provides an implementation of â Curricular Analyticsâ , a framework for analyzing and quantifying the complexity of academic curricula. Curricula are modelled as directed acyclic graphs and analytics are provided based on path lengths and edge density. This work directly comes from Heileman et al. (2018) <doi:10.48550/arXiv.1811.09676>.

An implementation of the clugen algorithm for generating multidimensional clusters with arbitrary distributions. Each cluster is supported by a line segment, the position, orientation and length of which guide where the respective points are placed. This package is described in Fachada & de Andrade (2023) <doi:10.1016/j.knosys.2023.110836>.

Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.

This package provides various tools of for clustering multivariate angular data on the torus. The package provides angular adaptations of usual clustering methods such as the k-means clustering, pairwise angular distances, which can be used as an input for distance-based clustering algorithms, and implements clustering based on the conformal prediction framework. Options for the conformal scores include scores based on a kernel density estimate, multivariate von Mises mixtures, and naive k-means clusters. Moreover, the package provides some basic data handling tools for angular data.

This package implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. Also provides the finite-sample RMCD method discussed in the paper, as well as the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> and Green and Martin (2017) <https://christopherggreen.github.io/papers/hr05_extension.pdf>. See also Chapter 2 of Green (2017) <https://digital.lib.washington.edu/researchworks/handle/1773/40304>.

Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.

Original ctsem (continuous time structural equation modelling) functionality, based on the OpenMx software, as described in Driver, Oud, Voelkle (2017) <doi:10.18637/jss.v077.i05>, with updated details in vignette. Combines stochastic differential equations representing latent processes with structural equation measurement models. These functions were split off from the main package of ctsem', as the main package uses the rstan package as a backend now -- offering estimation options from max likelihood to Bayesian. There are nevertheless use cases for the wide format SEM style approach as offered here, particularly when there are no individual differences in observation timing and the number of individuals is large. For the main ctsem package, see <https://cran.r-project.org/package=ctsem>.

Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) <doi:10.1002/env.2306>. Parameter estimates are identified by maximizing composite log-likelihood functions using the limited memory BFGS optimization algorithm with bounding constraints, while standard errors are obtained by estimating the Godambe information matrix.

This package performs forward model selection, using the C-index/concordance in survival analysis models.

This package provides simplified access to the data from the Catalog of Theses and Dissertations of the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES, <https://catalogodeteses.capes.gov.br>) for the years 1987 through 2022. The dataset includes variables such as Higher Education Institution (institution), Area of Concentration (area), Graduate Program Name (program_name), Type of Work (type), Language of Work (language), Author Identification (author), Abstract (abstract), Advisor Identification (advisor), Development Region (region), State (state).