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```
#' HITS Ranks
#' @description Estimate HITS ranks of nodes from an edge list or adjacency matrix. Returns a vector of ranks or (optionally) a list containing a vector for each mode. If the provided data is an edge list, this function returns ranks ordered by the unique values in the selected mode.
#'
#' @details Although originally designed for estimating ranks in unipartite graphs, HITS (Hyperlink-Induced Topic Search) is also one of the earliest bipartite ranking algorithms. Created by Jon Kleinberg (2009) \doi{10.1145/324133.324140} as an alternative to PageRank, HITS takes better account of the topology of bipartite networks by iteratively ranking nodes according to their role as an "Authority" and as a "Hub". Nodes with authority have high indegree from high ranking hubs; high ranking hubs have high outdegree to nodes with high authority. This function provides a slightly expanded version of HITS that only interfaces with bipartite networks and that allows for weighted edges. In general, HITS ranks tend to be more sensitive to user query than PageRanks, but HITS is substantially less efficient in ranking large graphs. HITS is likely less preferable than the other bipartite ranking algorithms in most applications. There are a number of contexts where HITS performs poorly, such as in graphs with extreme outliers.
#' @param data Data to use for estimating HITS. Must contain bipartite graph data, either formatted as an edge list (class data.frame, data.table, or tibble (tbl_df)) or as an adjacency matrix (class matrix or dgCMatrix).
#' @param sender_name Name of sender column. Parameter ignored if data is an adjacency matrix. Defaults to first column of edge list.
#' @param receiver_name Name of sender column. Parameter ignored if data is an adjacency matrix. Defaults to the second column of edge list.
#' @param weight_name Name of edge weights. Parameter ignored if data is an adjacency matrix. Defaults to edge weights = 1.
#' @param rm_weights Removes edge weights from graph object before estimating HITS. Parameter ignored if data is an edge list. Defaults to FALSE.
#' @param duplicates How to treat duplicate edges if any in data. Parameter ignored if data is an adjacency matrix. If option "add" is selected, duplicated edges and corresponding edge weights are collapsed via addition. Otherwise, duplicated edges are removed and only the first instance of a duplicated edge is used. Defaults to "add".
#' @param return_mode Mode for which to return HITS ranks. Defaults to "rows" (the first column of an edge list).
#' @param return_data_frame Return results as a data frame with node names in the first column and ranks in the second column. If set to FALSE, the function just returns a named vector of ranks. Defaults to TRUE.
#' @param alpha Dampening factor for first mode of data. Defaults to 0.85.
#' @param beta Dampening factor for second mode of data. Defaults to 0.85.
#' @param max_iter Maximum number of iterations to run before model fails to converge. Defaults to 200.
#' @param tol Maximum tolerance of model convergence. Defaults to 1.0e-4.
#' @param verbose Show the progress of this function. Defaults to FALSE.
#' @return A dataframe containing each node name and node rank. If return_data_frame changed to FALSE or input data is classed as an adjacency matrix, returns a vector of node ranks. Does not return node ranks for isolates.
#' @keywords Bipartite rank centrality HITS
#' @export
#' @import Matrix data.table
#' @md
#' @references
#' Jon M. Kleinberg. "Authoritative sources in a hyperlinked environment". *J. ACM*, 46(5):604-632, September 1999.
#' @examples
#' #create edge list between patients and providers
#' df <- data.table(
#' patient_id = sample(x = 1:10000, size = 10000, replace = TRUE),
#' provider_id = sample(x = 1:5000, size = 10000, replace = TRUE)
#' )
#'
#' #estimate HITS ranks
#' HITS <- br_hits(data = df)
br_hits <- function(
data,
sender_name = NULL,
receiver_name = NULL,
weight_name = NULL,
rm_weights= FALSE,
duplicates = c("add", "remove"),
return_mode = c("rows", "columns", "both"),
return_data_frame = TRUE,
alpha = 0.85,
beta = 0.85,
max_iter = 200,
tol = 1.0e-4,
verbose = FALSE
){
bipartite_rank(
data = data,
sender_name = sender_name,
receiver_name = receiver_name,
weight_name = weight_name,
rm_weights= rm_weights,
duplicates = duplicates,
normalizer = 'HITS',
return_mode = return_mode,
return_data_frame = return_data_frame,
alpha = alpha,
beta = beta,
max_iter = max_iter,
tol = tol,
verbose = verbose
)
}
```

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