smappnyu / mediascores Goto Github PK

checking R code for possible problems ... NOTE
  File ‘mediascores/R/zzz.R’:
    .onLoad calls:
      packageStartupMessage("\nmediascores: News-sharing Ideology from Social Media Link Data.\nVersion 0.0.1.9000 (Development Version)\ncopyright (c) 2019, Gregory Eady, New York University\n                    Fridolin Linder, New York University\nFor citation information, type citation(\"mediascores\").\n")
  
  See section ‘Good practice’ in '?.onAttach'.

 ERROR
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
  1/1 mismatches
  [1] 0.477 - 0.462 == 0.0157
  
  ── 2. Failure: point_est works (@test_mediascores.R#17)  ───────────────────────
  `theta_1_median` not equal to 0.46167.
  1/1 mismatches
  [1] 0.477 - 0.462 == 0.0157
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 2 SKIPPED: 0 FAILED: 2
  1. Failure: mediascores function produced a valid stanfit object (@test_mediascores.R#11) 
  2. Failure: point_est works (@test_mediascores.R#17) 

Warning messages:
1: The largest R-hat is 1.25, indicating chains have not mixed.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#r-hat 
2: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#bulk-ess 
3: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#tail-ess 

#' NetworkInference: Inferring latent diffusion networks
#' 
#' This package provides an R implementation of the \code{netinf} algorithm 
#' created by Gomez Rodriguez, Leskovec, and  Krause (2010). Given a set of
#' events that spread between a set of nodes the algorithm infers the most likely
#' stable diffusion network that is underlying the diffusion process.
#' 
#' The package provides three groups of functions: 1) data preparation 
#' 2) estimation and 3) interpretation.
#' 
#' @section Data preparation:
#' 
#' The core estimation function \code{\link{netinf}} requires an object of class 
#' \code{cascade} (see \link{as_cascade_long} and \link{as_cascade_wide}). 
#' Cascade data contains information on the potential nodes in the network as 
#' well as on event times for each node in each cascade. 
#' 
#' @section Estimation:
#' 
#' Diffusion networks are estimated using the \code{\link{netinf}} function. It 
#' produces a diffusion network in form of an edgelist (of class 
#' \code{\link{data.frame}}).
#' 
#' @section Interpretation and Visualization:
#' 
#' Cascade data can be visualized with the \code{plot} method of the \code{cascade}
#' class (\code{diffnet, \link{plot.cascade}}). Results of the estimation process can 
#' be visualized using the plotting method of the \code{diffnet} class. 
#' 
#' @section Performance:
#' 
#' If higher performance is required and for very large data sets, a faster pure C++ 
#' implementation is available in the Stanford Network Analysis Project (SNAP). 
#' The software can be downloaded at \url{http://snap.stanford.edu/netinf/}.
#' 
#' @useDynLib NetworkInference, .registration = TRUE
#' @importFrom Rcpp sourceCpp
#' @name NetworkInference
NULL

.onAttach <- function(...) {
      packageStartupMessage(
'NetworkInference: Inferring latent diffusion networks.
Version 1.2.3.9000 (Development Version)
copyright (c) 2016, Fridolin Linder, Pennsylvania State University
                    Bruce Desmarais, Pennsylvania State University
For citation information, type citation("NetworkInference").
Type help("NetworkInference") to get started.\n'
      )
  }