Merge pull request #33 from fipelle/dev

Updated docstrings
fipelle · Jan 6, 2022 · 6b5c370 · 6b5c370
2 parents ad3e525 + 0d43eb7
commit 6b5c370
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diff --git a/src/subsampling.jl b/src/subsampling.jl
@@ -7,7 +7,7 @@ Subsampling: Jackknife
 """
     block_jackknife(Y::Union{FloatMatrix, JMatrix{Float64}}, subsample::Float64)
 
-Generate block jackknife (Kunsch, 1989) samples. This implementation is described in Pellegrino (2020).
+Generate block jackknife (Kunsch, 1989) samples. This implementation is described in Pellegrino (2022).
 
 This technique subsamples a time series dataset by removing, in turn, all the blocks of consecutive observations with a given size.
 
@@ -16,7 +16,7 @@ This technique subsamples a time series dataset by removing, in turn, all the bl
 - `subsample`: Block size as a percentage of number of observed periods. It is bounded between 0 and 1.
 
 # References
-Kunsch (1989) and Pellegrino (2020).
+Kunsch (1989) and Pellegrino (2022).
 """
 function block_jackknife(Y::Union{FloatMatrix, JMatrix{Float64}}, subsample::Float64)
 
@@ -95,7 +95,7 @@ end
 """
     artificial_jackknife(Y::Union{FloatMatrix, JMatrix{Float64}}, subsample::Float64, max_samples::Int64, seed::Int64=1)
 
-Generate artificial jackknife samples as in Pellegrino (2020).
+Generate artificial jackknife samples as in Pellegrino (2022).
 
 The artificial delete-d jackknife is an extension of the delete-d jackknife for dependent data problems.
 - This technique replaces the actual data removal step with a fictitious deletion, which consists of imposing `d`-dimensional (artificial) patterns of missing observations to the data.
@@ -108,7 +108,7 @@ The artificial delete-d jackknife is an extension of the delete-d jackknife for
 - `seed`: Random seed (default: 1).
 
 # References
-Pellegrino (2020).
+Pellegrino (2022).
 """
 function artificial_jackknife(Y::Union{FloatMatrix, JMatrix{Float64}}, subsample::Float64, max_samples::Int64, seed::Int64=1)
 

diff --git a/src/types.jl b/src/types.jl
@@ -45,7 +45,7 @@ The state space model used below is,
 
 ``X_{t} = C*X_{t-1} + D*U_{t}``
 
-where ``e_{t} ~ N(0_{nx1}, R)`` and ``U_{t} ~ N(0_{mx1}, Q)``.
+where ``e_{t} \\sim N(0_{nx1}, R)`` and ``U_{t} \\sim N(0_{mx1}, Q)``.
 
 # Arguments
 - `Y`: Observed measurements (`nxT`)