populate tables

Author: kuraisle

src/data/algorithms.csv

@@ -24,7 +24,8 @@ $$t=\frac{\bar{x} - \mu_{0}}{s/\sqrt{n}}$$
 
 The sample mean, $\bar{x}$ can be calculated from the sum of the response variable and the count.
 The sample standard deviation can be calculated from the sum, sum of squares, and the count."
-algo_pmcc_decomp,stat_pmcc,Basic decomposition,Clients send sufficient summary statistics for calculating the statistic,none,,1,FALSE,client_to_aggregator,FALSE,FALSE,FALSE,none,none,,,29/10/2025 15:16,29/10/2025 15:16,"$$\rho_{X,Y}=\frac{cov(X,Y)}{\sigma_X \sigma_Y}$$
+algo_pmcc_decomp,stat_pmcc,Basic decomposition,Clients send sufficient summary statistics for calculating the statistic,fully,,1,FALSE,client_to_aggregator,FALSE,FALSE,FALSE,none,none,,,29/10/2025 15:16,29/10/2025 15:16,"$$\rho_{X,Y}=\frac{cov(X,Y)}{\sigma_X \sigma_Y}$$
 $cov$ is the covariance
 
-$\sigma_{X}$ is the standard deviation of $X$ and $\sigma_{Y}$ is the standard deviation of $Y$"
+$\sigma_{X}$ is the standard deviation of $X$ and $\sigma_{Y}$ is the standard deviation of $Y$"
+algo_var_iter,stat_variance,Iterative calculation,Iteratively calculate the variance using an aggregated mean.,iterative,$S^{2} = \frac{\sum{(x_{i}-\bar{x})^{2}}}{n-1}$,2,FALSE,bidirectional,FALSE,FALSE,FALSE,none,none,,,03/11/2025 12:13,03/11/2025 12:13,"The mean across samples ( $\bar{x}$ ) can be calculated separably with a round of aggregation, then sent to each client, which can then subtract the values from the sample mean ( $\sum{(x_{i}-\bar{x})^{2}}$ ) and send this value back to the aggregator."

src/data/observable_data.csv

@@ -19,4 +19,8 @@ obs_km_ds_times,algo_km_datashare,aggregator,Row-level time to event,Survival ti
 obs_cox_prop_ds_tte,algo_cox_prop_datashare,aggregator,Row-level time-to-event,The time-to-event (survival time) for each subject
 obs_cox_prop_ds_pred,algo_cox_prop_datashare,aggregator,Row-level predictor variables,Predictor variables for each subject
 obs_cov_ds_agg,algo_cov_datashare,aggregator,Row-level variable data,Data for each subject for each variable for calculating covariance
-obs_quantile_ds,algo_quantile_datashare,aggregator,Row-level variable,Row-level data for the variable for which the quartiles are calculated
+obs_quantile_ds,algo_quantile_datashare,aggregator,Row-level variable,Row-level data for the variable for which the quartiles are calculated
+obs_var_iter_count,algo_var_iter,aggregator,Sum,Local sum from each client
+obs_var_iter_sum,algo_var_iter,aggregator,Count,Local count from each client
+obs_var_iter_mean,algo_var_iter,other_clients,Mean,The aggregate mean across all clients
+obs_var_iter_ss,algo_var_iter,aggregator,Squared sum of differences,The squared sum of differences from the aggregate mean.