Formula object for specifying design matrices
Rmagic -- a cell magic for the python notebook using R code with python
jonathan-taylor/selective-inference 9
Software for selective inference
jonathan-taylor/profile_stats306b 3
Ipython profile for stats306b
Neuroimaging in python
jonathan-taylor/selective_compare 2
Comparison of approaches to selective inference
iPython Notebook + server application for delivering online homeworks
Comparisons between best subset selection and other popular estimators for sparse regression
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using 3.6
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adding tests for weightedl1 to compare with group lasso
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added converged method to quasi newton
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changing imports for sklearn
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BF: fix a divide by zero in root finding for bound form of l1
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BF: was using hessian instead of Q in solving equivalent direction problem
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erase disctinction between Q and hessian
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added bound form of weighted l1 and lagrange form of weighted sup
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added terms to weighted_l1/sup
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less precision in doctest
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cleanup of approx reference code
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add approx log reference
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added test for pivot b.o. approx reference
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added approximate ci b.o. approx reference
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sigma instead of sigma_sq while setting scale parameter of posterior samplers
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added hiv test: carved posterior interval estimates
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changing order of output of log posterior
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BF: changing prior as well
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BF: two more priors needed changing
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matching default prior
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using discrete family for approximate grid inference
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code cleanup for readability
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rename selective MLE method due to signature conflict
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unused grid arguments
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moved gaussian query specific methods to that class
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Merge branch 'snigdhagit-master' Merging approximate inference on a grid
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WIP: newton broken
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added a tolerance for determining inside set for when group_weight is 0
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fixed unpenalized groups to also check lasso_weights for sparse_group_lasso
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storing the loss for later use
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using negative deviance
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added full results notebook
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making output proper deviance
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removing debug statement
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adding some common penalties to api; BF: sklearn missing initial, some fixes of mean deviance and R2
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fix SyntaxWarning
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update paths, relative risk
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approximate mle examples, along with updated selective-inference repo
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added code for selective_mle related outputs
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added plots to the examples-MLE file
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including sim_xy directly in script
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updates on mle script
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Merge branch 'master' into snigdhagit-master
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adding a dispersion option to Lee et al lasso
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a few minor changes to test_cv_mle script -- to be replaced by an example in compare-selection
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Merge branch 'snigdhagit-master'
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script replaced by a notebook in compare-selection
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fixinf change of pval to pvalue
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change of lower/upper to lower_confidence/upper_confidence
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fixing plot
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update to scripts to run MLE examples
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renaming columns in output
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custom directory for snigdha
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using bestsubset instances
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adding concatenation
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made a batch script
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R code for plots
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missing file
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ryan sim.R file
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validation not really used for now
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selective-inference
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added point estimators as methods
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added conditional power (mean) in intervals summary
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added two separate batch files, mean for relative risk
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put premuted columns in right order while summarizing
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updating R and python code
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commit changes for now
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updating python code
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Merge pull request #2 from snigdhagit/snigdha_mle added point estimators as methods
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correcting subset of naive_selected
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Merge branch 'master' of https://github.com/jonathan-taylor/compare-selection into snigdha_mle
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changes for whoapsi talk'
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corrected prior
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added a local scaling for the Langevin sampler
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added prior var in test
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added both samplers to posterior inference
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fixed subgradient in split lasso
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posterior samplers-- some changes
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added test instances
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RF: worked on api for posterior sampling, merged snigdha's changes
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Merge branch 'snigdhagit-master'
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able to use degenerate gaussian randomization for e.g. followup LASSO
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some cleanup
commit sha 3932618e6f4e3328ecf36b7bd31273a067f7c062
using data frame summary output
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drop the losers query
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make sure we get no 0-sized samples
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import integer division
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Merge pull request #47 from jonathan-taylor/degenerate_random able to use degenerate gaussian randomization for e.g. followup LASSO
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import integer division
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make sure we get no 0-sized samples
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using data frame summary output
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drop the losers query
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some cleanup
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