Converters between Armadillo matrices (C++) and Numpy arrays using Pybind11
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Updated
Jun 12, 2024 - C++
Converters between Armadillo matrices (C++) and Numpy arrays using Pybind11
Explaining the output of machine learning models with more accurately estimated Shapley values
Rcpp integration for the Armadillo templated linear algebra library
An R package for predicting ploidal level from sequence data using site-based heterozygosity
An R package for Factor Model Asset Pricing
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. This is an unstable experimental version. Please see http://bioconductor.org/packages/BASiCS/ for the official release version
Rcpp and Travis-CI: Compiling Code with C++11
Rcpp integration for the Ensmallen templated C++ mathematical optimization library
Armadillo: fast C++ library for linear algebra & scientific computing - https://arma.sourceforge.net
R Package: Regularized Spatial Maximum Covariance Analysis
R Package: Regularized Principal Component Analysis for Spatial Data
C++ Implementation of poLCA (R package)
R Package: Adaptively weighted group lasso for semiparametic quantile regression models
Text Processing for Small or Big Data Files in R
Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering
Simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate (DINA) and reduced Reparameterized Unified Model (rRUM) from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.
The goal of rrum is to provide an implementation of Gibbs sampling algorithm for Bayesian Estimation of reduced Reparametrized Unifed Model (rRUM), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
Random generation library for distributions
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