### Conditional Knockoffs: Relaxing the Assumptions of Knockoffs

R code can be found here and tutorials can be found here for running conditional knockoffs, which are a way to run model-X knockoffs (with all the same guarantees and nearly as much power) without assuming the distribution of X is known, only that a flexible parametric model for the covariate distribution is known.

### Metropolized Knockoff Sampling

R and Python code can be found here and tutorials/notebooks can be found here for running the Metropolized knockoff sampler to flexibly construct exact model-X knockoffs using tools from the Markov chain Monte Carlo and graphical models literature.

### Model-X Knockoffs: High-Dimensional Controlled Variable Selection

R, Python, and MATLAB packages, as well as examples/vignettes can be found here for running model-X knockoffs to perform
variable selection while controlling the false discovery
rate, even in high dimensions and when the conditional model
for the response variable is unknown.

### EigenPrism: Inference for High-Dimensional Signal-to-Noise Ratios

R function for running
EigenPrism to compute confidence intervals for the norm of
the coefficient vector, noise level, or signal-to-noise
ratio in high-dimensional regression problems without
assuming sparsity or random effects.
MATLAB code
implementing the EigenPrism procedure and the simulations
and analyses in the paper.

### Familywise Error Rate Control Via Knockoffs

MATLAB
code
implementing knockoffs for familywise error rate control and
reproducing figures in paper. Knockoffs allows the user to
control the familywise error rate in linear regression
problems with more observations than variables.

### QUARTS: A Robust Method for Paleoclimate Reconstructions

R Code implementing QUAntile
Regression with Time Series errors (QUARTS). Includes script
for applying QUARTS to a Northern Hemisphere paleoclimate
reconstruction.

### Monte Carlo Motion Planning (MCMP)

This github
repository contains a Julia implementation of MCMP, which is an
algorithm for autonomously planning (in the presence of
uncertainty) a robot's trajectory through obstacles with a prespecified
lower-bound on the probability of success (e.g. 99%).

### Fast Marching Tree (FMT*)

The Open
Motion Planning Library contains an open-source
C++ implementation of FMT*. FMT* is an
asymptotically-optimal algorithm for autonomously planning a
robot's trajectory through obstacles.