Tests on properties of space-time covariance functions. Tests on symmetry, separability and for assessing different forms of non-separability are available. Moreover tests on some classes of covariance functions, such that the classes of product-sum models, Gneiting models and integrated product models have been provided. It is the companion R package to the papers of Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability and some classes of space-time covariance function models <doi:10.1007/s00477-017-1472-2> and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for selecting a class of space-time covariance functions <doi:10.18637/jss.v094.i01>.

Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a shiny interface. The Pharmacometric Modeling Language (PML) code updates in real time given changes to user inputs. Models can be executed using the Certara.RsNLME package. Additional support to generate the underlying Certara.RsNLME code to recreate the corresponding model in R is provided in the user interface.

Automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. In addition, a shiny App is provided, allowing users to analyze their data using an interactive graphical user interface, without any programming requirements.

Gives convenient access to publicly available police-recorded open crime data from large cities in the United States that are included in the Crime Open Database <https://osf.io/zyaqn/>.

Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions, with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++. For mathematical details and software tutorial, see Mahani and Sharabiani (2019) <DOI:10.18637/jss.v089.i09>.

Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) <doi:10.1177/1073191115621795> and functions for creating publication-ready tables and figures from the results.

Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the nimble package or from other packages. Plug-ins for JAGS via rjags and Stan via rstan are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components.

Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fieldsâ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).

Compute the certainty equivalents and premium risks as tools for risk-efficiency analysis. For more technical information, please refer to: Hardaker, Richardson, Lien, & Schumann (2004) <doi:10.1111/j.1467-8489.2004.00239.x>, and Richardson, & Outlaw (2008) <doi:10.2495/RISK080231>.

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 contains the CONCOR (CONvergence of iterated CORrelations) algorithm and a series of supplemental functions for easy running, plotting, and blockmodeling. The CONCOR algorithm is used on social network data to identify network positions based off a definition of structural equivalence; see Breiger, Boorman, and Arabie (1975) <doi:10.1016/0022-2496(75)90028-0> and Wasserman and Faust's book Social Network Analysis: Methods and Applications (1994). This version allows multiple relationships for the same set of nodes and uses both incoming and outgoing ties to find positions.

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>.

Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) <arXiv:2012.14159> are implemented.

This package provides a system for creating R Markdown reports with a sequential syntax.

Also abbreviates to "CCSeq". Finds clusters of colocalized sequences in .bed annotation files up to a specified cut-off distance. Two sequences are colocalized if they are within the cut-off distance of each other, and clusters are sets of sequences where each sequence is colocalized to at least one other sequence in the cluster. For a set of .bed annotation tables provided in a list along with a cut-off distance, the program will output a file containing the locations of each cluster. Annotated .bed files are from the pwmscan application at <https://ccg.epfl.ch/pwmtools/pwmscan.php>. Personal machines might crash or take excessively long depending on the number of annotated sequences in each file and whether chromsearch() or gensearch() is used.

It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) <doi:10.1016/j.jmva.2017.05.005> for the multivariate skew-normal case.

This package provides a device closing function which is able to crop graphics (e.g., PDF, PNG files) on Unix-like operating systems with the required underlying command-line tools installed.

Utilities that support the usage of pyDarwin (<https://certara.github.io/pyDarwin/>) for ease of setup and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) modeling engine.

This package provides functions for microbiome data analysis that take into account its compositional nature. Performs variable selection through penalized regression for both, cross-sectional and longitudinal studies, and for binary and continuous outcomes.

Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.

This package provides functions and data files to help CE Public-Use Microdata (PUMD) users calculate annual estimated expenditure means, standard errors, and quantiles according to the methods used by the CE with PUMD. For more information on the CE please visit <https://www.bls.gov/cex>. For further reading on CE estimate calculations please see the CE Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of Methods at <https://www.bls.gov/opub/hom/cex/calculation.htm>. For further information about CE PUMD please visit <https://www.bls.gov/cex/pumd.htm>.

Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015> (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data.

Puzzle game that can be played in the R console. Help the alien to find the ship.

Direct sparse covariance matrix estimation via the covariance graphical lasso by Bien, Tibshirani (2011) <doi:10.1093/biomet/asr054> using the fast coordinate descent algorithm of Wang (2014) <doi:10.1007/s11222-013-9385-5>.