diff --git a/yli/survival.py b/yli/survival.py
index 4fba928..b5d8586 100644
--- a/yli/survival.py
+++ b/yli/survival.py
@@ -135,7 +135,7 @@ def calc_survfunc_kaplanmeier(time, status, ci, transform_x=None, transform_y=No
 	
 	return xpoints, ypoints, None, None
 
-def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn', fig=None, ax=None):
+def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, maxiter=None, fail_prob_tolerance=None, se_method=None, zero_tolerance=None, transform_x=None, transform_y=None, nan_policy='warn', fig=None, ax=None):
 	"""
 	Generate a Turnbull estimator plot, which extends the Kaplan–Meier estimator to interval-censored observations
 	
@@ -157,6 +157,14 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, trans
 	:type ci: bool
 	:param step_loc: Proportion along the length of each Turnbull interval to step down the survival function, e.g. 0 for left bound, 1 for right bound, 0.5 for interval midpoint
 	:type step_loc: float
+	:param maxiter: Maximum number of iterations to attempt
+	:type maxiter: int
+	:param fail_prob_tolerance: Terminate algorithm when the absolute change in failure probability in each interval is less than this tolerance
+	:type fail_prob_tolerance: float
+	:param se_method: Method for computing standard error or survival probabilities (see hpstat *turnbull* documentation)
+	:type se_method: str
+	:param zero_tolerance: Threshold for dropping failure probability when se_method is "oim-drop-zeros"
+	:type zero_tolerance: float
 	:param transform_x: Function to transform x axis by
 	:type transform_x: callable
 	:param transform_y: Function to transform y axis by
@@ -187,11 +195,11 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, trans
 		
 		for group in groups.groups:
 			subset = groups.get_group(group)
-			handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], ci, step_loc, transform_x, transform_y)
+			handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], ci, step_loc, maxiter, fail_prob_tolerance, se_method, zero_tolerance, transform_x, transform_y)
 			handle.set_label('{} = {}'.format(by, group))
 	else:
 		# No grouping
-		plot_survfunc_turnbull(ax, df[time_left], df[time_right], ci, step_loc, transform_x, transform_y)
+		plot_survfunc_turnbull(ax, df[time_left], df[time_right], ci, step_loc, maxiter, fail_prob_tolerance, se_method, zero_tolerance, transform_x, transform_y)
 	
 	if time_units:
 		ax.set_xlabel('{} + {} ({})'.format(time_left, time_right, time_units))
@@ -206,8 +214,8 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, trans
 	
 	return fig, ax
 
-def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, transform_x=None, transform_y=None):
-	xpoints, ypoints, ypoints0, ypoints1 = calc_survfunc_turnbull(time_left, time_right, ci, step_loc, transform_x, transform_y)
+def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, maxiter=None, fail_prob_tolerance=None, se_method=None, zero_tolerance=None, transform_x=None, transform_y=None):
+	xpoints, ypoints, ypoints0, ypoints1 = calc_survfunc_turnbull(time_left, time_right, ci, step_loc, maxiter, fail_prob_tolerance, se_method, zero_tolerance, transform_x, transform_y)
 	
 	handle = ax.plot(xpoints, ypoints)[0]
 	
@@ -216,12 +224,23 @@ def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, transfor
 	
 	return handle
 
-def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, transform_x=None, transform_y=None):
+def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, maxiter=None, fail_prob_tolerance=None, se_method=None, zero_tolerance=None, transform_x=None, transform_y=None):
 	# Estimate the survival function
 	
 	# Prepare arguments
-	# TODO: Pass through other arguments
 	hpstat_args = [config.hpstat_path, 'turnbull', '-', '--output', 'json']
+	if maxiter:
+		hpstat_args.append('--max-iterations')
+		hpstat_args.append(str(maxiter))
+	if fail_prob_tolerance:
+		hpstat_args.append('--fail-prob-tolerance')
+		hpstat_args.append(str(fail_prob_tolerance))
+	if se_method:
+		hpstat_args.append('--se-method')
+		hpstat_args.append(se_method)
+	if zero_tolerance:
+		hpstat_args.append('--zero-tolerance')
+		hpstat_args.append(str(zero_tolerance))
 	
 	# Export data to CSV
 	csv_buf = io.StringIO()