Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.

Splits data into Gaussian type clusters using the Cross-Entropy Clustering ('CEC') method. This method allows for the simultaneous use of various types of Gaussian mixture models, for performing the reduction of unnecessary clusters, and for discovering new clusters by splitting them. CEC is based on the work of Spurek, P. and Tabor, J. (2014) <doi:10.1016/j.patcog.2014.03.006>.

CGAL is a C++ library that aims to provide easy access to efficient and reliable algorithms in computational geometry. Since its version 4, CGAL can be used as standalone header-only library and is available under a double GPL-3|LGPL license. <https://www.cgal.org/>.

The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, <doi:10.1002/sim.8909>.

Calculate the theoretical value of convertible bonds by given parameters, including B-S theory and Monte Carlo method.

This package implements cointegration tests with structural breaks designed for small sample sizes, following the methodology of Trinh (2022) <https://ideas.repec.org/p/ema/worpap/2022-01.html>. Supports models with no breaks, breaks in constant only, and breaks in both constant and slope. Provides endogenous break date detection using ADF or SSR minimization criteria, with small-sample adjusted critical values via response surface methodology.

This package performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.

This package provides functions for making contour plots. The contour plot can be created from grid data, a function, or a data set. If non-grid data is given, then a Gaussian process is fit to the data and used to create the contour plot.

An R implementation of the algorithms described in Reingold and Dershowitz (4th ed., Cambridge University Press, 2018) <doi:10.1017/9781107415058>, allowing conversion between many different calendar systems. Cultural and religious holidays from several calendars can be calculated.

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.

This package provides tools for estimation and clustering of spherical data, seamlessly integrated with the flexmix package. Includes the necessary M-step implementations for both Poisson Kernel-Based Distribution (PKBD) and spherical Cauchy distribution. Additionally, the package provides random number generators for PKBD and spherical Cauchy distribution. Methods are based on Golzy M., Markatou M. (2020) <doi:10.1080/10618600.2020.1740713>, Kato S., McCullagh P. (2020) <doi:10.3150/20-bej1222> and Sablica L., Hornik K., Leydold J. (2023) <doi:10.1214/23-ejs2149>.

Copula-based regression models for multivariate censored data, including bivariate right-censored data, bivariate interval-censored data, and right/interval-censored semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and semiparametric (Cox and transformation) models. Includes methods for convenient prediction and plotting. Also provides a bivariate time-to-event simulation function and an information ratio-based goodness-of-fit test for copula. Method details can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and Sun et al. (2023+) JRSSC.

Imports PxStat data in JSON-stat format and (optionally) reshapes it into wide format. The Central Statistics Office (CSO) is the national statistical institute of Ireland and PxStat is the CSOs online database of Official Statistics. This database contains current and historical data series compiled from CSO statistical releases and is accessed at <https://data.cso.ie>. The CSO PxStat Application Programming Interface (API), which is accessed in this package, provides access to PxStat data in JSON-stat format at <https://data.cso.ie>. This dissemination tool allows developers machine to machine access to CSO PxStat data.

This package provides tools for crop breeding analysis including Genetic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation (PCV), heritability, genetic advance calculations, stability analysis using the Eberhart-Russell model, two-way ANOVA for genotype-environment interactions, and Additive Main Effects and Multiplicative Interaction (AMMI) analysis. These tools are developed for crop breeding research and stability evaluation under various environmental conditions. The methods are based on established statistical and biometrical principles. Refer to Eberhart and Russell (1966) <doi:10.2135/cropsci1966.0011183X000600010011x> for stability parameters, Fisher (1935) "The Design of Experiments" <ISBN:9780198522294>, Falconer (1996) "Introduction to Quantitative Genetics" <ISBN:9780582243026>, and Singh and Chaudhary (1985) "Biometrical Methods in Quantitative Genetic Analysis" <ISBN:9788122433764> for foundational methodologies.

Utilities to make your clinical collaborations easier if not fun. It contains functions for designing studies such as Simon 2-stage and group sequential designs and for data analysis such as Jonckheere-Terpstra test and estimating survival quantiles.

This package performs copy number variants association analysis with Lasso and Weighted Fusion penalized regression. Creates a "CNV profile curve" to represent an individualâ s CNV events across a genomic region so to capture variations in CNV length and dosage. When evaluating association, the CNV profile curve is directly used as a predictor in the regression model, avoiding the need to predefine CNV loci. CNV profile regression estimates CNV effects at each genome position, making the results comparable across different studies. The penalization encourages sparsity in variable selection with a Lasso penalty and encourages effect smoothness between consecutive CNV events with a weighted fusion penalty, where the weight controls the level of smoothing between adjacent CNVs. For more details, see Si (2024) <doi:10.1101/2024.11.23.624994>.

Measuring cellular energetics is essential to understanding a matrixâ s (e.g. cell, tissue or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization using the methods described by Mookerjee et al. (2017) <doi:10.1074/jbc.m116.774471>.

Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.

This package provides functions to construct finite-sample calibrated predictive intervals for Bayesian models, following the approach in Barber et al. (2021) <doi:10.1214/20-AOS1965>. These intervals are calculated efficiently using importance sampling for the leave-one-out residuals. By default, the intervals will also reflect the relative uncertainty in the Bayesian model, using the locally-weighted conformal methods of Lei et al. (2018) <doi:10.1080/01621459.2017.1307116>.

An implementation of Fan plots for cytometry data in ggplot2'. For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).

Weekly notified dengue cases and climate variables in Colombo district Sri Lanka from 2008/ week-52 to 2014/ week-21.

This package provides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) <doi:10.1002/sim.6880>. Pure within-cluster and preferential within-cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors.

This package provides functions to simplify the process of preparing event and transaction for cohort analysis.

Evaluation of the Carlson elliptic integrals and the incomplete elliptic integrals with complex arguments. The implementations use Carlson's algorithms <doi:10.1007/BF02198293>. Applications of elliptic integrals include probability distributions, geometry, physics, mechanics, electrodynamics, statistical mechanics, astronomy, geodesy, geodesics on conics, and magnetic field calculations.