This package provides functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.

This package implements the Polynomial Maximization Method ('PMM') for parameter estimation in linear and time series models when error distributions deviate from normality. The PMM2 variant achieves lower variance parameter estimates compared to ordinary least squares ('OLS') when errors exhibit significant skewness. Includes methods for linear regression, AR'/'MA'/'ARMA'/'ARIMA models, and bootstrap inference. Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) <doi:10.1007/978-3-319-77179-3_75>, Zabolotnii, Tkachenko, and Warsza (2022) <doi:10.1007/978-3-031-03502-9_37>, and Zabolotnii, Tkachenko, and Warsza (2023) <doi:10.1007/978-3-031-25844-2_21>.

Perform a Bayesian estimation of the exploratory reduced reparameterized unified model (ErRUM) described by Culpepper and Chen (2018) <doi:10.3102/1076998618791306>.

This package provides tools for importing, analyzing and visualizing ego-centered network data. Supports several data formats, including the export formats of EgoNet', EgoWeb 2.0 and openeddi'. An interactive (shiny) app for the intuitive visualization of ego-centered networks is provided. Also included are procedures for creating and visualizing Clustered Graphs (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).

It contains functions for dose calculation for different routes, fitting data to probability distributions, random number generation (Monte Carlo simulation) and calculation of systemic and carcinogenic risks. For more information see the publication: Barrio-Parra et al. (2019) "Human-health probabilistic risk assessment: the role of exposure factors in an urban garden scenario" <doi:10.1016/j.landurbplan.2019.02.005>.

Augments the eiCompare package's Racially Polarized Voting (RPV) functionality to streamline analyses and visualizations used to support voting rights and redistricting litigation. The package implements methods described in Barreto, M., Collingwood, L., Garcia-Rios, S., & Oskooii, K. A. (2022). "Estimating Candidate Support in Voting Rights Act Cases: Comparing Iterative EI and EI-RÃ C Methods" <doi:10.1177/0049124119852394>.

Computes maximum mean discrepancy two-sample test for univariate data using the Laplacian kernel, as described in Bodenham and Kawahara (2023) <doi:10.1007/s11222-023-10271-x>. The p-value is computed using permutations. Also includes implementation for computing the robust median difference statistic Q_n from Croux and Rousseeuw (1992) <doi:10.1007/978-3-662-26811-7_58> based on Johnson and Mizoguchi (1978) <doi:10.1137/0207013>.

The main function, ProtectTable(), performs table suppression according to a frequency rule with a data set as the only required input. Within this function, protectTable(), protect_linked_tables() or runArgusBatchFile() in package sdcTable is called. Lists of level-hierarchy (parameter dimList') and other required input to these functions are created automatically. The suppression method Gauss (default) is implemented independently of sdcTable'. The function, PTgui(), starts a graphical user interface based on the shiny package.

EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.

An interface for performing climate matching using the Euclidean "Climatch" algorithm. Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the â Climatchâ algorithm, also referred to as the â Climateâ algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). â Climatch user manualâ . The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). <doi:10.1111/ddi.12391>.

This package provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge <https://www.synapse.org/ProstateCancerChallenge> presented in Guinney J, Wang T, Laajala TD, et al. (2017) <doi:10.1016/S1470-2045(16)30560-5> is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects.

Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2022) <doi:10.1201/9780429503764>.

Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.

Use emailjs API easily in R'. This package is not official. <https://www.emailjs.com/docs/rest-api/send/>. You can send e-mail with emailjs with function, based on httr'. You can also make a shiny ui and server function. It can be used for making feedback form, inquiry, and so on.

Implementation of the Centre of Gravity method and the Extrapolated Centre of Gravity method. It supports replicated observations. Cameron, D.G., et al (1982) <doi:10.1366/0003702824638610> JCGM (2008) <doi:10.59161/JCGM100-2008E>.

This package provides functions to create simulated time series of environmental exposures (e.g., temperature, air pollution) and health outcomes for use in power analysis and simulation studies in environmental epidemiology. This package also provides functions to evaluate the results of simulation studies based on these simulated time series. This work was supported by a grant from the National Institute of Environmental Health Sciences (R00ES022631) and a fellowship from the Colorado State University Programs for Research and Scholarly Excellence.

Streamlines the fitting of common Bayesian item response models using Stan.

This package provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function effectstars there exist methods for special objects, for example for vglm objects from the VGAM package.

This package provides step-by-step automation for integrating biodiversity data from multiple online aggregators, merging and cleaning datasets while addressing challenges such as taxonomic inconsistencies, georeferencing issues, and spatial or environmental outliers. Includes functions to extract environmental data and to define the biogeographic ranges in which species are most likely to occur.

This package provides a collection of fast and flexible functions for analyzing omics data in observational studies. Multiple different approaches for integrating multiple environmental/genetic factors, omics data, and/or phenotype data are implemented. This includes functions for performing omics wide association studies with one or more variables of interest as the exposure or outcome; a function for performing a meet in the middle analysis for linking exposures, omics, and outcomes (as described by Chadeau-Hyam et al., (2010) <doi:10.3109/1354750X.2010.533285>); and a function for performing a mixtures analysis across all omics features using quantile-based g-Computation (as described by Keil et al., (2019) <doi:10.1289/EHP5838>).

Import data from Epidata XML files .epx and convert it to R data structures.

This package provides functions for eleven procedures for determining the number of factors, including functions for parallel analysis and the minimum average partial test. There are also functions for conducting principal components analysis, principal axis factor analysis, maximum likelihood factor analysis, image factor analysis, and extension factor analysis, all of which can take raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, gamma correlations, or polychoric correlations. Varimax rotation, promax rotation, and Procrustes rotations can be performed. Additional functions focus on the factorability of a correlation matrix, the congruences between factors from different datasets, the assessment of local independence, the assessment of factor solution complexity, and internal consistency. Auerswald & Moshagen (2019, ISSN:1939-1463); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2); Mulaik (2010, ISBN:978-1-4200-9981-2); O'Connor (2000, <doi:10.3758/bf03200807>); O'Connor (2001, ISSN:0146-6216).

An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. Bayesian inference is done via MCMC sampling using Stan <https://mc-stan.org/>.