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> #testing generate_net, get_statistics, Jeong, Newman_corrected and PAFit
> 
> library(PAFit)
> for (prob_m in c("TRUE", "FALSE"))
+    for (inc in c("TRUE","FALSE"))
+       for (log in c("TRUE", "FALSE"))
+  
+               for (i in 1:3) {
+                   net  <- generate_net(N = 50, m = 10,prob_m = prob_m, increase = inc, log = log,
+                                       mode = i, s = 10)
+                   for (bin in c("TRUE","FALSE"))
+                     
+                     for (deg_thresh in c(0)) {  
+                         net_stats <- get_statistics(net,deg_threshold = deg_thresh, binning = bin, g = 10) 
+                         #check stats
+                         if (sum(net_stats$m_t) != sum(net_stats$sum_m_k))
+                             stop("wrong at m_t and sum_m_k")
+                         if (sum(abs(colSums(net_stats$m_tk) - net_stats$sum_m_k)) != 0)
+                             stop("wrong at m_tk and sum_m_k")
+                         temp <- sapply(1:(net_stats$T-1),function(x) sum(net_stats$node_degree[x,] != -1))
+                         if (sum(abs(rowSums(net_stats$n_tk) - (rowSums(net_stats$offset_tk) + temp))))
+                             stop("wrong at node_degree, n_tk, offset_tk") 
+                         if (sum(net_stats$z_j) > sum(net_stats$m_t))
+                             stop("wrong at z_j")   
+                         if (sum(net_stats$z_j) + sum(net_stats$offset_m_tk) - sum(net_stats$sum_m_k))
+                             stop("Wrong at offset_m_tk") 
+                         net_stats <- get_statistics(net,deg_threshold = deg_thresh, binning = bin, g = 10,only_PA = TRUE)
+                         net_stats <- get_statistics(net,deg_threshold = deg_thresh, binning = bin, g = 10,only_true_deg_matrix = TRUE)
+                     }
+               }
> net_stats <- get_statistics(net,deg_threshold = deg_thresh, binning = TRUE, g = 10) 
> print(net_stats)
Contains summary statistics for the temporal network.
> summary(net_stats)
Contains summary statistics for the temporal network.
Type of network: directed 
Number of nodes in the final network: 50 
Number of edges in the final network: 480 
Number of new nodes: 49 
Number of new edges: 479 
Number of time-steps: 49 
Maximum in-degree: 131 
Number of bins: 10 
> for (mode_f_value in c("Constant_PA", "Log_linear")) {
+                         result_Jeong  <- Jeong(net,net_stats, T_0_start = 0, T_0_end = 20, T_1_start = 30 , T_1_end = 40)
+                         result_Jeong  <- Jeong(net,net_stats, T_0_start = 0, T_0_end = 20, T_1_start = 30 , T_1_end = 40, interpolate = TRUE)
+                         print(result_Jeong)
+                         summary(result_Jeong)
+                         plot(result_Jeong,net_stats)
+                         plot(result_Jeong,net_stats, line = TRUE)
+                         plot(result_Jeong,net_stats, high_deg = 5)
+                         result_Newman <- Newman(net, net_stats)
+                         result_Newman <- Newman(net, net_stats, interpolate = TRUE)
+                       
+ }

Containing the estimated attachment function. 
Number of bins: 10 
Estimated attachment exponent: 0.3508132 
Attachment exponent ± 2 s.d. : (-0.1049892,0.8066156)

Containing the estimated attachment function. 
Number of bins: 10 
Estimated attachment exponent: 0.3508132 
Attachment exponent ± 2 s.d.: (-0.1049892,0.8066156)

Containing the estimated attachment function. 
Number of bins: 10 
Estimated attachment exponent: 0.3508132 
Attachment exponent ± 2 s.d. : (-0.1049892,0.8066156)

Containing the estimated attachment function. 
Number of bins: 10 
Estimated attachment exponent: 0.3508132 
Attachment exponent ± 2 s.d.: (-0.1049892,0.8066156)
> net_stats <- get_statistics(net,deg_threshold = deg_thresh, binning = FALSE, g = 10) 
> print(net_stats)
Contains summary statistics for the temporal network.
> summary(net_stats)
Contains summary statistics for the temporal network.
Type of network: directed 
Number of nodes in the final network: 50 
Number of edges in the final network: 480 
Number of new nodes: 49 
Number of new edges: 479 
Number of time-steps: 49 
Maximum in-degree: 131 
Number of bins: 132 
> for (mode_f_value in c("Constant_PA", "Log_linear")) {
+ 
+   result_Jeong  <- Jeong(net,net_stats, T_0_start = 0, T_0_end = 20, T_1_start = 30 , T_1_end = 40)
+   result_Jeong  <- Jeong(net,net_stats, T_0_start = 0, T_0_end = 20, T_1_start = 30 , T_1_end = 40, interpolate = TRUE)
+   print(result_Jeong)
+   summary(result_Jeong)
+   plot(result_Jeong,net_stats)
+   plot(result_Jeong,net_stats, line = TRUE)
+   plot(result_Jeong,net_stats, high_deg = 5)
+   result_Newman <- Newman(net, net_stats)
+   result_Newman <- Newman(net, net_stats, interpolate = TRUE)
+ }

Containing the estimated attachment function. 
Number of bins: 132 
Estimated attachment exponent: 0.3741427 
Attachment exponent ± 2 s.d. : (-0.06417573,0.8124611)

Containing the estimated attachment function. 
Number of bins: 132 
Estimated attachment exponent: 0.3741427 
Attachment exponent ± 2 s.d.: (-0.06417573,0.8124611)

Containing the estimated attachment function. 
Number of bins: 132 
Estimated attachment exponent: 0.3741427 
Attachment exponent ± 2 s.d. : (-0.06417573,0.8124611)

Containing the estimated attachment function. 
Number of bins: 132 
Estimated attachment exponent: 0.3741427 
Attachment exponent ± 2 s.d.: (-0.06417573,0.8124611)
> 
> 
> proc.time()
   user  system elapsed 
   3.10    0.56    3.65