This package implements a spatial Bayesian non-parametric factor analysis model with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). Spatial correlation is introduced in the columns of the factor loadings matrix using a Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional autoregressive (CAR) prior and point-referenced spatial data is treated using a Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using Polya-Gamma augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), <doi:10.1214/20-BA1253> in Bayesian Analysis.

Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.

Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.

Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.

Simultaneously infers state-dependent diversification across two or more states of a single or multiple traits while accounting for the role of a possible concealed trait. See Herrera-Alsina et al. (2019) <doi:10.1093/sysbio/syy057>.

Implementation of the Conditional Least Square (CLS) estimates and its covariance matrix for the first-order spatial integer-valued autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012) <doi:10.1080/03610926.2011.560739>.

Markov chain Monte Carlo samplers for posterior simulations of conjugate Bayesian nonparametric mixture models. Functionality is provided for Gibbs sampling as in Algorithm 3 of Neal (2000) <DOI:10.1080/10618600.2000.10474879>, restricted Gibbs merge-split sampling as described in Jain & Neal (2004) <DOI:10.1198/1061860043001>, and sequentially-allocated merge-split sampling <DOI:10.1080/00949655.2021.1998502>, as well as summary and utility functions.

The Patient Rule Induction Method (PRIM) is typically used for "bump hunting" data mining to identify regions with abnormally high concentrations of data with large or small values. This package extends this methodology so that it can be applied to binary classification problems and used for prediction.

The explosion of biobank data offers immediate opportunities for gene-environment (GxE) interaction studies of complex diseases because of the large sample sizes and rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in GxE assessment, especially for set-based GxE variance component (VC) tests, a widely used strategy to boost overall GxE signals and to evaluate the joint GxE effect of multiple variants from a biologically meaningful unit (e.g., gene). We present SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based GxE tests, to permit GxE VC test scalable to biobank data. SEAGLE employs modern matrix computations to achieve the same â exactâ results as the original GxE VC tests, and does not impose additional assumptions nor relies on approximations. SEAGLE can easily accommodate sample sizes in the order of 10^5, is implementable on standard laptops, and does not require specialized equipment. The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, Lin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.

It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arXiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.

Manage package documentation and namespaces from the command line. Programmatically attach namespaces in R and Rmd script, populates Roxygen2 skeletons with information scraped from within functions and populate the Imports field of the DESCRIPTION file.

Stagewise techniques implemented with Generalized Estimating Equations to handle individual, group, bi-level, and interaction selection. Stagewise approaches start with an empty model and slowly build the model over several iterations, which yields a path of candidate models from which model selection can be performed. This slow brewing approach gives stagewise techniques a unique flexibility that allows simple incorporation of Generalized Estimating Equations; see Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017) <doi:10.1111/biom.12669> for details.

Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).

Data sets used by Krause et al. (2022) <doi:10.1101/2022.04.11.487885>. It comprises phenotypic records obtained from the USDA Northern Region Uniform Soybean Tests from 1989 to 2019 for maturity groups II and III. In addition, soil and weather variables are provided for the 591 observed environments (combination of locations and years).

Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).

Useful to visualize the Poissoneity (an independent Poisson statistical framework, where each RNA measurement for each cell comes from its own independent Poisson distribution) of Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore cell clustering based on model departure as a novel data representation.

Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating the squared deviations from sample variance. Besides indicating the contribution of squared deviations for the sample variability, these plots are capable of detecting characteristics of the distribution such as symmetry, skewness and outliers. A remarkable graphical method based on Sv-plot2 can determine the decision on testing hypotheses over one or two population means. In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.

This package provides a simple wrapper to easily design vanilla deep neural networks using Tensorflow'/'Keras backend for regression, classification and multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding, feature selection and anomaly detection).

This package provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7]. Main references: [1] van Genuchten (1980) <doi:10.2136/sssaj1980.03615995004400050002x>, [2] Mualem (1976) <doi:10.1029/WR012i003p00513>, [3] Peters (2013) <doi:10.1002/wrcr.20548>, [4] Iden and Durner (2013) <doi:10.1002/2014WR015937>, [5] Peters (2014) <doi:10.1002/2014WR015937>, [6] Wind G. P. (1966), [7] Peters and Durner (2008) <doi:10.1016/j.jhydrol.2008.04.016> and [8] Brooks and Corey (1964).

Shiny Module to create, visualize, customize and export Excel-like pivot table.

This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.

This package provides a sparse covariance estimator based on different thresholding operators.

Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>).

This is an interface for the Python package StepMix'. It is a Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings. Software paper available at <doi:10.18637/jss.v113.i08>.