alex hayes

How to bind many columns to node data at once?

3

So far I've got the following but I'm wondering if there's a better approach: ``` r library(tidygraph) #> Warning: package 'tidygraph' was built under R version 3.5.1 #> #> Attaching...

Depth of node in tree

2

I have nodes that correspond to time ordered events. I don't know the exact times, but the edges are directed, and a node can only connect to older nodes. So...

Randomized SVD

1

I presume this is a long shot/not a priority, but do you have any plans to implement a randomized SVD solver? I see that there's https://github.com/erichson/rSVD but it's just in...

Expose C++ multiplication interface

2

Hey there! My lab spends a lot of time taking SVDs of, let's say, "nearly nice" matrices. For these matrices, we can often compute `t(A) %*% x` and `A %*%...

Utility to immediately provide shareable link to data

7

I've been using the following for a little while to quickly share small datasets via Google Drive. Would love to submit a PR, not sure about the best final form...

Allowed parameter documentation is ... sparse

1

I had a really hard time figuring out what acceptable values are for parameters. Often times the documentation is unclear with regards to: - Acceptable type - Bounds on numerics...

Namespacing issue with MARS models

1

I shouldn't need to explicitly load `earth` but I do. ``` r library(parsnip) mars( mode = "classification", num_terms = 1, prod_degree = 1, prune_method = "backward" ) %>% set_engine("earth") %>%...

### Describe the solution you'd like In many functions we can save results to disk as we query them from the API. It does not (to my understanding) seem like...

Questions: missing data behavior, general advice

2

## Missing data behavior Does `prediction` provide any guarantees about predictions on missing data? I've been playing around some and it seems like most methods behave like `predict()` with `na.action...

Running into the following during some simulations, possibly related to #1. ``` r library(RSpectra) M [1] 40 47 sum(M) #> [1] 50 image(as.matrix(M)) ```  ``` r M #> 40...