RcppGO is an optimization package for R written in C++
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Updated
Dec 14, 2021 - R
RcppGO is an optimization package for R written in C++
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Jointly model the accuracy of cognitive responses and item choices within a bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.
Docker image: rocker/tidyverse + Rcpp +RcppArmadillo + remotes
Implementation of R package for the travelling salesman problem.
Como fazer seu código R ficar mais rápido com Rcpp
R Package: Adaptively weighted group lasso for semiparametic quantile regression models
R package for 'Variational Mode Decomposition' (1- and 2-dimensional)
Template for RcppArmadillo-dependent packages
Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) <https://doi.org/10.1007/s11336-019-09693-2>
A place to keep useful Rcpp and RcppArmadillo implementations of various random number generators and math functions.
multinomial random effects
R and C++ code useful for time series analysis on dichotomous data
R package: Computes the solution path of the multivariate Scalar-on-Functional Elastic Net regression in serial and parallel.
A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>. This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.
Efficient sampling of normal posterior distributions
R package that creates Bayesian I- and D-optimal designs for choice models involving mixtures of ingredient proportions
R implementation of two samples location/scale multidimensional test proposed by Barale and Shirke.
R package containing helpful functions for dealing with spatial data and concliques.
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