amen - Additive and Multiplicative Effects Models for Networks and Relational Data

Analysis of dyadic network and relational data using additive and multiplicative effects (AME) models. The basic model includes regression terms, the covariance structure of the social relations model (Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>, Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>). Several different link functions accommodate different relational data structures, including binary/network data, normal relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from fixed-rank nomination schemes. Several of these link functions are discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) <DOI:10.1017/nws.2013.17>. Development of this software was supported in part by NIH grant R01HD067509.

Last updated 4 years ago

6.76 score 27 stars 143 scripts 507 downloads

rstiefel - Random Orthonormal Matrix Generation and Optimization on the Stiefel Manifold

Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.

Last updated 3 years ago

6.43 score 3 stars 7 packages 86 scripts 309 downloads

Rduino - A Microcontroller Interface

Functions for connecting to and interfacing with an 'Arduino' or similar device. Functionality includes uploading of sketches, setting and reading digital and analog pins, and rudimentary servo control. This project is not affiliated with the 'Arduino' company, <https://www.arduino.cc/>.

Last updated 7 years ago

3.73 score 2 stars 18 scripts 127 downloads

FABInference - FAB p-Values and Confidence Intervals

Frequentist assisted by Bayes (FAB) p-values and confidence interval construction. See Hoff (2019) <arXiv:1907.12589> "Smaller p-values via indirect information", Hoff and Yu (2019) <doi:10.1214/18-EJS1517> "Exact adaptive confidence intervals for linear regression coefficients", and Yu and Hoff (2018) <doi:10.1093/biomet/asy009> "Adaptive multigroup confidence intervals with constant coverage".

Last updated 4 years ago

3.40 score 5 stars 1 scripts 114 downloads

sbgcop - Semiparametric Bayesian Gaussian Copula Estimation and Imputation

Estimation and inference for parameters in a Gaussian copula model, treating the univariate marginal distributions as nuisance parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. This package also provides a semiparametric imputation procedure for missing multivariate data.

Last updated 7 years ago

3.12 score 1 packages 44 scripts 244 downloads

covKCD - Covariance Estimation for Matrix Data with the Kronecker-Core Decomposition

Matrix-variate covariance estimation via the Kronecker-core decomposition. Computes the Kronecker and core covariance matrices corresponding to an arbitrary covariance matrix, and provides an empirical Bayes covariance estimator that adaptively shrinks towards the space of separable covariance matrices. For details, see Hoff, McCormack and Zhang (2022) <arXiv:2207.12484> "Core Shrinkage Covariance Estimation for Matrix-variate data".

Last updated 2 years ago

3.00 score 2 stars 260 downloads

eigenmodel - Semiparametric Factor and Regression Models for Symmetric Relational Data

Estimation of the parameters in a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accommodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification. See Hoff (2007) <arXiv:0711.1146> for details on the model.

Last updated 6 years ago

2.78 score 5 packages 22 scripts 1.8k downloads

fabCI - FAB Confidence Intervals

Frequentist assisted by Bayes (FAB) confidence interval construction. See 'Adaptive multigroup confidence intervals with constant coverage' by Yu and Hoff <DOI:10.1093/biomet/asy009> and 'Exact adaptive confidence intervals for linear regression coefficients' by Hoff and Yu <DOI:10.1214/18-EJS1517>.

Last updated 4 years ago

2.00 score 7 scripts 159 downloads