John S Bogaardt

Yes, you can use the `Development` estimator to omit any link ratios and those should propagate to the heatmap. ```python import chainladder as cl cl.Development(n_periods=3).fit_transform(cl.load_sample('raa')).link_ratio.heatmap() ```

Cool. By your approach you don't even need the `Development` estimator: ```python raa = cl.load_sample('raa') raa.link_ratio[raa.link_ratio.valuation.year >= raa.valuation_date.year-3].heatmap() ```

Should this be reclassifed discussion forum Q&A?

I fear n_diagonals will be short lived. Eventually someone will want the same but with drop_hi and drop_low. I dont want to create more args for those use cases. Maybe...

Hi @cf4869 , this is related to #230 and #231. When using discrete valuations `C(valuation)`, the regression doesn't quite know how to handle valuation periods beyond those present in the...

I'll have to dust off the paper - its been a while and my understanding is a little hazy. Fitting features as ordinal/continuous and not strictly categorical, you get a...

Do you mean to insert offsets for specific parameters rather than fitting the parameters from the data? Unfortunately, no. Under the hood, the regression is being carried out by [sklearn.linearmodel.Linear_Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression)...

This should work. Marking as bug. ```python import chainladder as cl import io import pandas as pd df_sub = pd.read_csv( io.StringIO(""" 2011-01-01, 2011-01-01, 179.74 2011-01-01, 2011-07-01, 664.94 2011-01-01, 2012-01-01, 7471.75...

Yes, I know it doesn't work, sorry for not being clearer. I just reformatted your code to have a REPREX for testing purposes. This is a bug.

Is this a good starting point for a reproducible example? ```python import chainladder as cl import numpy as np raa = cl.load_sample('raa') raa.iat[..., 3, :] = np.nan # Zero out...