Construct directed graphs of S4 class hierarchies and visualize them. In general, these graphs typically are DAGs (directed acyclic graphs), often simple trees in practice.

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

Compile inline C code and easily call with automatically generated wrapper functions. By allowing user-defined headers and compilation flags (preprocessor, compiler and linking flags) the user can configure optimization options and linking to third party libraries. Multiple functions may be defined in a single block of code - which may be defined in a string or a path to a source file.

Cointegration methods are widely used in empirical macroeconomics and empirical finance. It is well known that in a cointegrating regression the ordinary least squares (OLS) estimator of the parameters is super-consistent, i.e. converges at rate equal to the sample size T. When the regressors are endogenous, the limiting distribution of the OLS estimator is contaminated by so-called second order bias terms, see e.g. Phillips and Hansen (1990) <DOI:10.2307/2297545>. The presence of these bias terms renders inference difficult. Consequently, several modifications to OLS that lead to zero mean Gaussian mixture limiting distributions have been proposed, which in turn make standard asymptotic inference feasible. These methods include the fully modified OLS (FM-OLS) approach of Phillips and Hansen (1990) <DOI:10.2307/2297545>, the dynamic OLS (D-OLS) approach of Phillips and Loretan (1991) <DOI:10.2307/2298004>, Saikkonen (1991) <DOI:10.1017/S0266466600004217> and Stock and Watson (1993) <DOI:10.2307/2951763> and the new estimation approach called integrated modified OLS (IM-OLS) of Vogelsang and Wagner (2014) <DOI:10.1016/j.jeconom.2013.10.015>. The latter is based on an augmented partial sum (integration) transformation of the regression model. IM-OLS is similar in spirit to the FM- and D-OLS approaches, with the key difference that it does not require estimation of long run variance matrices and avoids the need to choose tuning parameters (kernels, bandwidths, lags). However, inference does require that a long run variance be scaled out. This package provides functions for the parameter estimation and inference with all three modified OLS approaches. That includes the automatic bandwidth selection approaches of Andrews (1991) <DOI:10.2307/2938229> and of Newey and West (1994) <DOI:10.2307/2297912> as well as the calculation of the long run variance.

There are several non-functional-form-based interaction tests for testing interaction in unreplicated two-way layouts. However, no single test can detect all patterns of possible interaction and the tests are sensitive to a particular pattern of interaction. This package combines six non-functional-form-based interaction tests for testing additivity. These six tests were proposed by Boik (1993) <doi:10.1080/02664769300000004>, Piepho (1994), Kharrati-Kopaei and Sadooghi-Alvandi (2007) <doi:10.1080/03610920701386851>, Franck et al. (2013) <doi:10.1016/j.csda.2013.05.002>, Malik et al. (2016) <doi:10.1080/03610918.2013.870196> and Kharrati-Kopaei and Miller (2016) <doi:10.1080/00949655.2015.1057821>. The p-values of these six tests are combined by Bonferroni, Sidak, Jacobi polynomial expansion, and the Gaussian copula methods to provide researchers with a testing approach which leverages many existing methods to detect disparate forms of non-additivity. This package is based on the following published paper: Shenavari and Kharrati-Kopaei (2018) "A Method for Testing Additivity in Unreplicated Two-Way Layouts Based on Combining Multiple Interaction Tests". In addition, several sentences in help files or descriptions were copied from that paper.

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.

Computes conditional multivariate t probabilities, random deviates, and densities. It can also be used to create missing values at random in a dataset, resulting in a missing at random (MAR) mechanism. Inbuilt in the package are the Expectation-Maximization (EM), Monte Carlo EM, and Stochastic EM algorithms for imputation of missing values in datasets assuming the multivariate t distribution. See Kinyanjui, Tamba, Orawo, and Okenye (2020)<doi:10.3233/mas-200493>, and Kinyanjui, Tamba, and Okenye(2021)<http://www.ceser.in/ceserp/index.php/ijamas/article/view/6726/0> for more details.

Reads and writes CSV with selected conventions. Uses the same generic function for reading and writing to promote consistent formats.

This package provides a suite of computer model test functions that can be used to test and evaluate algorithms for Bayesian (also known as sequential) optimization. Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package.

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. This package is maintained for consistency with the original ctsem paper, but for the much newer and more capable ctsem package, see <https://cran.r-project.org/package=ctsem>.

Providing data to quickly visualize and analyze data from several cryptocurrencies.

For ordinal rating data, estimate and test models within the family of CUB models and their extensions (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions); Simulation routines, plotting facilities and fitting measures are also provided.

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>.

Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.

Clustering multi-subject resting state functional Magnetic Resonance Imaging data. This methods enables the clustering of subjects based on multi-subject resting state functional Magnetic Resonance Imaging data. Objects are clustered based on similarities and differences in cluster-specific estimated components obtained by Independent Component Analysis.

Create CUSUM (cumulative sum) statistics from a vector or dataframe. Also create single or faceted CUSUM control charts, with or without control limits. Accepts vector, dataframe, tibble or data.table inputs.

This package provides a tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.

Calculates the credit debt for the next period based on the available data using the cross-classification credibility model.

Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.

Flexible framework for coalescent analyses in R. It includes a main function running the MCMC algorithm, auxiliary functions for tree rearrangement, and some functions to compute population genetic parameters. Extended description can be found in Paradis (2020) <doi:10.1201/9780429466700>. For details on the MCMC algorithm, see Kuhner et al. (1995) <doi:10.1093/genetics/140.4.1421> and Drummond et al. (2002) <doi:10.1093/genetics/161.3.1307>.

This package provides a minimal interface for applying annotators from the Stanford CoreNLP java library. Methods are provided for tasks such as tokenisation, part of speech tagging, lemmatisation, named entity recognition, coreference detection and sentiment analysis.

Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.

This package provides a Bayesian approach to using predictive probability in an ANOVA construct with a continuous normal response, when threshold values must be obtained for the question of interest to be evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). The Bayesian Mission Mean (BMM) is used to evaluate a question of interest (that is, a mean that randomly selects combination of factor levels based on their probability of occurring instead of averaging over the factor levels, as in the grand mean). Under this construct, in contrast to a Gibbs sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is required. The nested sampler determines the conditional posterior distribution of the model parameters, given Y, and the outside sampler determines the marginal posterior distribution of Y (also commonly called the predictive distribution for Y). This approach provides a sample from the joint posterior distribution of Y and the model parameters, while also accounting for the threshold value that must be obtained in order for the question of interest to be evaluated as successful.

Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. The package makes it easy to incorporate constraints into the model's coefficients. The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), a vector of bound constraints for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions among coefficients (i.e beta1 > beta2). The references of this packages are the same as stats package for glm() and arima() functions. See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528). For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages.