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Hello,
Thank you for developing STITCH. It looks like a very promising program with better performance to impute rare variants.
I was trying to use it on a rice population of 3,500 lines with an…
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When a region is missing wind or solar profile data, we first attempt to impute wind and solar profiles based on averaging the wind or solar profile from directly-interconnected balancing authorities …
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Hello! I am using Remiod for my dissertation project. The data I have got has continuous outcomes with missing values, the aim is to model the data as MAR and MNAR (Delta, J2R and CR). When I include …
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## Univariate
This is easy for univariate metrics. Add `na.rm` as an argument and use complete cases.
- [ ] `util_moments()`
- [ ] `util_totals()`
- [ ] `util_proportions()`
- [ ] `util_perc…
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Both `adjustedsurv` and `adjustedcif` support multiple imputation via `mids` objects. However, the current variance estimator is not consistent and does not use Rubin's rules. Both functions currently…
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### What happened + What you expected to happen
It seems that intermittent data causes issues with `predict_insample()`. If any intervals are missing all data, then the problem becomes clear: the ran…
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related to #943
943 and related discussion is on how to handle outliers. AFAICS, the main approach for outlier detection and handling for ARMA and similar models are based intervention models, and …
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Run into this error will calling mice.resuse(testdata) :
Reproducible Example:
```
sampledata = data.frame( Group1 = rep( c("A","B"),20) , "Group2" = c(rep("C",20),rep("D",20)))
sampledata["Me…
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## Participants
- @findanna , FIND
- @diabatem-find, FIND
- @andreranza, cynkra
## Tasks
- **create data model for non-high-risk countries**
- **imputation of missings in gf procurement ac…
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Hi,I want to know the result of mae and rsme after new dataset was added to the test. In utils.py you define a function named imputation_accuracy but you don‘t use it in the example using heart datase…