Confidence sequences and uniform boundaries
Project description
Confidence sequences and uniform boundaries
This library supports calculation of uniform boundaries, confidence sequences, and alwaysvalid pvalues. These constructs are useful in sequential A/B testing, bestarm identification, and other sequential statistical procedures. The library is written in C++ with Python and R interfaces. The main reference is
Howard, S. R., Ramdas, A., McAuliffe, J., and Sekhon, J. (2018), Uniform, nonparametric, nonasymptotic confidence sequences, preprint, arXiv:1810.08240.
Additionally, the library includes some functions for quantile confidence sequences and A/B testing based on
Howard, S. R. and Ramdas, A. (2019), Sequential estimation of quantiles with applications to A/Btesting and bestarm identification, preprint, arXiv:1906.09712.
This library is in earlystage development and should not be considered stable. I have tested it only on Python 3.7.0 and R 3.6.1 on macOS Mojave. The implementation is in C++ and a compiler with C++14 support is required to build the package, as well as the Boost C++ headers.
In the Python package, functions are split across modules by topic, as detailed below. In the R package, all functions mentioned below are exported in a single namespace.
Installing the python package
Run pip3 install confseq
at the command line.
Installing the R package
Run the following in the R console:
install.packages('devtools') devtools::install_github('gostevehoward/confseq/r_package')
Demos
Estimating average treatment effect in a randomized trial
demo/ate_demo.py
illustrates how to compute a confidence sequence for average
treatment effect in a randomized trial with bounded potential outcomes, along
with an alwaysvalid pvalue sequence. The method is based on Corollary 2 of the
paper and uses the gammaexponential mixture boundary. This demo requires
numpy
and pandas
.
Quantile confidence sequences
demo/quantiles.py
illustrates how to use some of the included boundaries to
construct confidence sequences for quantiles based on a stream of
i.i.d. samples. The file includes a function to estimate a single, fixed
quantile, as well as a function to estimate all quantiles simultaneously, with
error control uniform over quantiles and time.
Uniform boundaries
The confseq.boundaries
Python module implements several uniform boundaries
from the confidence sequences paper.

There are four mixture boundaries. These are implemented by the functions
<TYPE>_log_mixture()
and<TYPE>_mixture_bound()
, where<TYPE>
is one ofnormal
(Propositions 4 and 5),gamma_exponential
(Proposition 8),gamma_poisson
(Proposition 9), orbeta_binomial
(Propositions 6 and 7).<TYPE>_log_mixture(s, v, ...)
returns the logarithm of the mixture supermartingale when called with S_t, the martingale, and V_t, the intrinsic time process. The reciprocal of the exponential of this value is an alwaysvalid pvalue. These functions are denoted log(m(s,v)) in the paper.<TYPE>_mixture_bound(v, alpha, ...)
returns the uniform boundary with crossing probability at most alpha, evaluated at intrinsic time v.
Each function takes another required argument
v_opt
and an optional argumentalpha_opt=0.05
. These arguments are used to set the tuning parameter for each mixture, denoted by rho or r in the paper, optimizing the uniform boundary with crossing probabilityalpha_opt
for intrinsic timev_opt
. Such tuning is discussed in section 3.5 of the paper.The gammaexponential and gammaPoisson mixtures also require a scale parameter
c
. The betabinomial mixture requires range parametersg
andh
. Finally, thenormal_*
andbeta_binomial_*
functions accept an optional boolean parameteris_one_sided
which isTrue
by default. IfFalse
, the twosided variants of these mixtures are used (Propositions 4 and 6). 
The polynomial stitching boundary (see Theorem 1 and the subsequent example) is implemented by
poly_stitching_bound
. Besidesv
andalpha
, this function requires the tuning parameterv_min
as well as optional parametersc
,s
, andeta
, all documented in the paper. 
This module also includes a
bernoulli_confidence_interval
function which computes confidence sequences for the mean of any distribution with bounded support by making use of the subBernoulli condition. Observations must be scaled so that the support is within the unit interval [0, 1].
All functions accept NumPy arrays in Python or vectors in R and perform vectorized operations.
Quantile bounds
The confseq.quantiles
Python module implements two quantileuniform confidence
sequences from the quantile paper.
empirical_process_lil_bound
is based on Theorem 2, and can be used to construct iteratedlogarithmrate confidence sequences for quantiles in which the confidence radius (in quantile space) is constant for all quantiles. This can also be used run the sequential KolmogorovSmirnov test described in section 7.2.double_stitching_bound
is based on Theorem 3, and can be used to construct confidence sequences for quantiles in which the confidence radius (in quantile space) varies, getting smaller for extreme quantiles close to zero and one.
Finally, quantile_ab_p_value
implements the twosided sequential test of the
hypothesis that two populations have equal values for some quantile, based on
Theorem 5. The theorem covers tests of null hypothesis other than equality, as
well as onesided tests, but these are not yet implemented.
C++ library
The underlying implementation is in a singlefile, headeronly C++ library in
src/confseq/uniform_boundaries.h
. The top of the file defines a simplified
interface mirroring the Python interface described above. Below that is an
objectoriented interface useful for more involved work. The
confseq.boundaries
Python module is a wrapper generated by
pybind11. The R package uses
Rcpp.
Unit tests
Run make C /path/to/confseq/tests runtests
to run the C++ unit tests.
Citing this software
Howard, S. R., and Ramdas, A. (2019), ConfSeq: software for confidence sequences and uniform boundaries, https://github.com/gostevehoward/confseq [Online; accessed 20190808].
@Misc{, author = {Steven R. Howard and Aaditya Ramdas}, title = {{ConfSeq}: software for confidence sequences and uniform boundaries}, year = {2019}, url = "https://github.com/gostevehoward/confseq", note = {[Online; accessed <today>]} }
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Filename, size  File type  Python version  Upload date  Hashes 

Filename, size confseq0.0.5cp37cp37mmacosx_10_13_x86_64.whl (314.4 kB)  File type Wheel  Python version cp37  Upload date  Hashes View 
Filename, size confseq0.0.5.tar.gz (8.0 kB)  File type Source  Python version None  Upload date  Hashes View 
Hashes for confseq0.0.5cp37cp37mmacosx_10_13_x86_64.whl
Algorithm  Hash digest  

SHA256  ea0c25c4df2c99cdccc09a41e58c5f185fbf9d4e0e2fe85de7e2cf4b9a3f03d3 

MD5  2d225254b757d1352181e6c85fb25489 

BLAKE2256  10626900e06c22678ad6069dfe0d3b7900cbfa82aa33ba199f870baa47d13f33 