remove "Written by" comments, fixes
@@ -69,12 +69,6 @@ def approxLognormal(N, mu=0.0, sigma=1.0, tail_N=0, tail_bound=[0.02,0.98], tail
d : DiscreteDistribution
Probability associated with each point in array of discrete
points for discrete probability mass function.
-
- Written by Luca Gerotto
- Based on Matab function "setup_workspace.m," from Chris Carroll's
- [Solution Methods for Microeconomic Dynamic Optimization Problems]
- (http://www.econ2.jhu.edu/people/ccarroll/solvingmicrodsops/) toolkit.
- Latest update: 11 February 2017 by Matthew N. White
'''
# Find the CDF boundaries of each segment
if sigma > 0.0:
@@ -145,12 +139,6 @@ def approxMeanOneLognormal(N, sigma=1.0, **kwargs):
d : DiscreteDistribution
Probability associated with each point in array of discrete
points for discrete probability mass function.
-
- Written by Nathan M. Palmer
- Based on Matab function "setup_shocks.m," from Chris Carroll's
- [Solution Methods for Microeconomic Dynamic Optimization Problems]
- (http://www.econ2.jhu.edu/people/ccarroll/solvingmicrodsops/) toolkit.
- Latest update: 01 May 2015
'''
mu_adj = - 0.5*sigma**2;
dist = approxLognormal(N=N, mu=mu_adj, sigma=sigma, **kwargs)
@@ -368,9 +356,6 @@ def makeTauchenAR1(N, sigma=1.0, rho=0.9, bound=3.0):
Grid points on which the discretized process takes values
trans_matrix: np.array
Markov transition array for the discretized process
-
- Written by Edmund S. Crawley
- Latest update: 27 October 2017
'''
yN = bound*sigma/((1-rho**2)**0.5)
y = np.linspace(-yN,yN,N)
@@ -411,9 +396,6 @@ def addDiscreteOutcomeConstantMean(distribution, x, p, sort = False):
d : DiscreteDistribution
Probability associated with each point in array of discrete
points for discrete probability mass function.
-
- Written by Matthew N. White
- Latest update: 08 December 2015 by David Low
'''
X = np.append(x,distribution.X*(1-p*x)/(1-p))
pmf = np.append(p,distribution.pmf*(1-p))
@@ -444,9 +426,6 @@ def addDiscreteOutcome(distribution, x, p, sort = False):
d : DiscreteDistribution
Probability associated with each point in array of discrete
points for discrete probability mass function.
-
- Written by Matthew N. White
- Latest update: 11 December 2015
'''
X = np.append(x,distribution.X)
@@ -483,9 +462,6 @@ def combineIndepDstns(*distributions):
X_out: np.array (as many as in *distributions)
Discrete points for the joint discrete probability mass function.
-
- Written by Nathan Palmer
- Latest update: 5 July August 2017 by Matthew N White
'''
# Get information on the distributions
dist_lengths = ()
