Segmentation Trees in Partial Least Squares Path Modeling
companion package of plspm with approaches of segmentation
trees in PLS Path Modeling.

Gaston Sanchez <plspmr@gmail.com>, and Tomas Aluja
<tomas.aluja@upc.edu>

Pathmox Approach of Segmentation Trees in Partial Least Squares
Path Modeling
c(
person("Gaston", "Sanchez",
email = "gaston.stat@gmail.com", role=c("aut", "cre")),
person("Tomas", "Aluja", role=c("aut", "ths")))
pathmox, the cousin package of plspm, provides a very
interesting solution for handling segmentation variables
in PLS Path Modeling: segmentation trees in PLS Path Modeling.
'fix.pathmox.R' 'fix.techmox.R' 'pathmox-internal.R'
'pathmox.R' 'plot.bootnodes.R' 'plot.treemox.R'
'plot.treemox.pls.R' 'plspmox.R' 'print.bootnodes.R'
'print.treemox.R' 'print.treemox.pls.R' 'techmox.R'
'treemox.boot.R' 'treemox.pls.R' 'get_fix_xexeloa.R'
'get_nominal_split.R' 'get_ordinal_split.R' 'get_pls_basic.R'
'get_xexeloa.R' 'test_sanchez_aluja.R' 'check_pathmox_args.r'
Gaston Sanchez [aut, cre],
Tomas Aluja [aut, ths]

Richard Cotton [aut, cre], Janko Thyson [ctb]
c(person("Richard", "Cotton", role = c("aut", "cre"),
email = "richierocks@gmail.com"), person("Janko", "Thyson", role = "ctb"))
Utilities for paths, files and directories.

assertive.base (>= 0.0-3), assertive.properties,
assertive.types, assertive.files, assertive.reflection,
assertive.strings, plyr, stringr (>= 1.0.0), utils

assertive.base (>= 0.0-3), assertive.files, assertive.numbers,
assertive.properties, assertive.reflection, assertive.strings,
assertive.types, magrittr, plyr, stringi, utils

An Imputation Approach to Estimating Path-Specific Causal
Effects
c(
person("Minh", "Trinh", email = "mdtrinh@mit.edu", role = "cre"),
person("Teppei", "Yamamoto", email = "teppei@mit.edu", role = "aut"),
person("Xiang", "Zhou", email = "xiang_zhou@fas.harvard.edu", role = "aut"))
In causal mediation analysis with multiple causally ordered mediators, a set of path-specific
effects are identified under standard ignorability assumptions. This package implements an imputation
approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis
(Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating
path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified
using the nonparametric bootstrap.
BART, boot, gbm, ggplot2, metR, parallel, pryr, stats, twang,
tidyr
Minh Trinh [cre],
Teppei Yamamoto [aut],
Xiang Zhou [aut]