Fits Weighted Quantile Sum (WQS) regression (Carrico et al. (2014) <doi:10.1007/s13253-014-0180-3>), a random subset implementation of WQS (Curtin et al. (2019) <doi:10.1080/03610918.2019.1577971>), a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) and a WQS with 2 indices (Renzetti et al. (2023) <doi:10.3389/fpubh.2023.1289579>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.
When the response variable Y takes one of R > 1 values, the function glsm() computes the maximum likelihood estimates (MLEs) of the parameters under four models: null, complete, saturated, and logistic. It also calculates the log-likelihood values for each model. This method assumes independent, non-identically distributed variables. For grouped data with a multinomial outcome, where observations are divided into J populations, the function glsm() provides estimation for any number K of explanatory variables.
Retrieving regional plant checklists, species traits and distributions, and environmental data from the Global Inventory of Floras and Traits (GIFT). More information about the GIFT database can be found at <https://gift.uni-goettingen.de/about> and the map of available floras can be visualized at <https://gift.uni-goettingen.de/map>. The API and associated queries can be accessed according the following scheme: <https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.
Suite of functions to study animal incubation. At the core of incR lies an algorithm that allows for the scoring of incubation behaviour. Additionally, several functions extract biologically relevant metrics of incubation such as off-bout number and off-bout duration - for a review of avian incubation studies, see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.
This package provides a PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>.
An implementation of modified maximum contrast methods (Sato et al. (2009) <doi:10.1038/tpj.2008.17>; Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>) and the maximum contrast method (Yoshimura et al. (1997) <doi:10.1177/009286159703100213>): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte Carlo method with pmvt() function of package mvtnorm', and mmcm.resamp() gives P-value by using a permutation method.
The word Meme was originated from the book, The Selfish Gene', authored by Richard Dawkins (1976). It is a unit of culture that is passed from one generation to another and correlates to the gene, the unit of physical heredity. The internet memes are captioned photos that are intended to be funny, ridiculous. Memes behave like infectious viruses and travel from person to person quickly through social media. The meme package allows users to make custom memes.
This package implements the Quantitative Classification-based on Association Rules (QCBA) algorithm (<doi:10.1007/s10489-022-04370-x>). QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. Supported are CBA implementations from rCBA', arulesCBA and arc packages, and CPAR', CMAR', FOIL2 and PRM implementations from arulesCBA package and SBRL implementation from the sbrl package. The result of the post-processing is an ordered CBA-like rule list.
This package provides a coalescent simulator that allows the rapid simulation of biological sequences under neutral models of evolution, see Staab et al. (2015) <doi:10.1093/bioinformatics/btu861>. Different to other coalescent based simulations, it has an optional approximation parameter that allows for high accuracy while maintaining a linear run time cost for long sequences. It is optimized for simulating massive data sets as produced by Next- Generation Sequencing technologies for up to several thousand sequences.
This package provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including Latent Transition Analysis (LTA; Linda M. Collins and Stephanie T. Lanza, 2009) <doi:10.1002/9780470567333>, Latent Class Profile Analysis (LCPA; Hwan Chung et al., 2010) <doi:10.1111/j.1467-985x.2010.00674.x>, and Joint Latent Class Analysis (JLCA; Saebom Jeon et al., 2017) <doi:10.1080/10705511.2017.1340844>, and any other extended models involving multiple latent class variables.
This package implements the TRUH test statistic for two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020). <DOI:10.1214/20-AOAS1362> for more details.
Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank correlations. Test statistics as well as one sided or two sided p-values are provided for all correlations. Multiple correlations and p-values can be calculated simultaneously across multiple variables. In addition, users will have the option to use the partial, semipartial, and generalized partial correlations; where the partial and semipartial correlations use linear, logistic, or Poisson regression to modify the specified variable.
Wrap-around Time Series (WATS) plots for interrupted time series designs with seasonal patterns. Longitudinal trajectories are shown in both Cartesian and polar coordinates. In many scenarios, a WATS plot more clearly shows the existence and effect size of of an intervention. This package accompanies "Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs" by Rodgers, Beasley, & Schuelke (2014) <doi:10.1080/00273171.2014.946589>; see citation("Wats") for details.
La libreria ACEP contiene funciones especificas para desarrollar analisis computacional de eventos de protesta. Asimismo, contiene base de datos con colecciones de notas sobre protestas y diccionarios de palabras conflictivas. Coleccion de diccionarios que reune diccionarios de diferentes origenes. The ACEP library contains specific functions to perform computational analysis of protest events. It also contains a database with collections of notes on protests and dictionaries of conflicting words. Collection of dictionaries that brings together dictionaries from different sources.
This package implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) <doi:10.1177/0962280219844288>.
The meaning of adea is "alternate DEA". This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. <doi:10.1590/0101-7438.2018.038.01.0031>. A full functional on-line and interactive version is available at <https://knuth.uca.es/shiny/DEA/>.
Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.
Offers a flexible formula-based interface for building and training Bayesian Neural Networks powered by Stan'. The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) <doi:10.1007/978-1-4612-0745-0>.
Agreement of continuously scaled measurements made by two techniques, devices or methods is usually evaluated by the well-established Bland-Altman analysis or plot. Conditional method agreement trees (COAT), proposed by Karapetyan, Zeileis, Henriksen, and Hapfelmeier (2025) <doi:10.1093/jrsssc/qlae077>, embed the Bland-Altman analysis in the framework of recursive partitioning to explore heterogeneous method agreement in dependence of covariates. COAT can also be used to perform a Bland-Altman test for differences in method agreement.
Fit latent variable linear models, estimating score distributions for groups of people, following Cohen and Jiang (1999) <doi:10.2307/2669917>. In this model, a latent distribution is conditional on students item response, item characteristics, and conditioning variables the user includes. This latent trait is then integrated out. This software is intended to fit the same models as the existing software AM <https://am.air.org/>. As of version 2, also allows the user to draw plausible values.
This package provides a wide collection of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. The data are related to different applied domains, including medical, reliability analysis, engineering, manufacturing, occupational safety, geological sciences, terrorism, psychology, agriculture, environmental sciences, road traffic accidents, demography, actuarial science, law, and justice. The documentation, along with associated references for further details and uses, is presented.
Simulates and estimates the Exponential Random Partition Model presented in the paper Hoffman, Block, and Snijders (2023) <doi:10.1177/00811750221145166>. It can also be used to estimate longitudinal partitions, following the model proposed in Hoffman and Chabot (2023) <doi:10.1016/j.socnet.2023.04.002>. The model is an exponential family distribution on the space of partitions (sets of non-overlapping groups) and is called in reference to the Exponential Random Graph Models (ERGM) for networks.
This package provides functions for calculating metrics for the measurement biodiversity and its changes across scales, treatments, and gradients. The methods implemented in this package are described in: Chase, J.M., et al. (2018) <doi:10.1111/ele.13151>, McGlinn, D.J., et al. (2019) <doi:10.1111/2041-210X.13102>, McGlinn, D.J., et al. (2020) <doi:10.1101/851717>, and McGlinn, D.J., et al. (2023) <doi:10.1101/2023.09.19.558467>.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them. This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.