progressr, fastmap, base64url, base64enc, synchronicity,
Suggests: microbenchmark, htmltools, knitr, rmarkdown, testthat,
promises, magrittr, glue, qs, RcppRedis, future.callr
Packaged: 2020-06-22 23:50:13 UTC; beauchamplab

digest, rlang (>= 0.4.0), startup, rstudioapi (>= 0.11)
promises, magrittr, glue, yaml, qs, RcppRedis, future.callr,
Packaged: 2020-07-17 03:21:44 UTC; beauchamplab

Packaged: 2020-08-12 00:51:18 UTC; beauchamplab

Packaged: 2020-10-09 06:24:21 UTC; beauchamplab

Packaged: 2021-01-09 01:07:06 UTC; beauchamplab

progressr, fastmap (>= 1.1.0), base64url, base64enc,
synchronicity, digest, rlang (>= 0.4.0), startup, rstudioapi
Packaged: 2021-02-13 09:02:32 UTC; beauchamplab

Packaged: 2021-02-14 21:49:41 UTC; beauchamplab

Packaged: 2021-02-19 02:10:11 UTC; beauchamplab

person("Zhengjia", "Wang", email = "dipterix.wang@gmail.com", role = c("aut", "cre"))

Suggests: knitr, rmarkdown, testthat, microbenchmark, magrittr, yaml,

Imports: utils, graphics, grDevices, parallel, Rcpp, RcppParallel, R6,
shiny, cli, stringr, jsonlite (>= 1.6), future, future.apply,

Title: Hartigan's dip test statistic for unimodality - corrected code
Description: Compute Hartigan's dip test statistic for unimodality
based on Fortran and S-plus from Dario Ringach (NYU.edu)

Packaged: Thu Aug 12 14:09:55 2004; maechler

Packaged: Mon Feb  9 15:31:22 2009; maechler

Author: Martin Maechler (based on Fortran and S-plus from Dario

Author: Martin Maechler (originally from Fortran and S-plus by Dario

Author: Martin Maechler (originally from Fortran and S-plus by Dario Ringach, NYU.edu)

Description: Compute Hartigan's dip test statistic for unimodality /
multimodality and provide a test with simulation based p-values.

Title: Hartigan's Dip Test Statistic for Unimodality - Corrected
multimodality and provide a test with simulation based p-values,  where
the original public code has been corrected.

VersionNote: Last CRAN: 0.75-7 on 2015-06-07

Title: Hartigan's dip test statistic for unimodality

Title: Debiased Inverse Propensity Score Weighting
Description: Estimation of the average treatment effect when controlling for
high-dimensional confounders using debiased inverse propensity score
weighting (DIPW). DIPW relies on the propensity score following a sparse
logistic regression model, but the regression curves are not required to be
estimable. Despite this, our package also allows the users to estimate
the regression curves and take the estimated curves as input to our
methods. Details of the methodology can be found in Yuhao Wang and
Rajen D. Shah (2020) "Debiased Inverse Propensity Score Weighting for
Estimation of Average Treatment Effects with High-Dimensional Confounders"
<arXiv:2011.08661>. The package relies on the optimisation
software 'MOSEK' <https://www.mosek.com/> which must be installed separately;
person("Yuhao", "Wang", email = "yuhaow.thu@gmail.com", role = c("cre", "aut")),
person("Rajen", "Shah", email = "r.shah@statslab.cam.ac.uk", role = "ctb"))
Imports: glmnet, Rmosek, Matrix, methods, stats