Title: Automatic Linear and Logistic Regression and Survival Analysis
Description: Make summary tables for descriptive statistics and select explanatory variables
automatically in various regression models. Support linear models, generalized linear
models and cox-proportional hazard models. Generate publication-ready tables summarizing
result of regression analysis and plots. The tables and plots can be exported in "HTML",
"pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.
Imports: moonBook(>= 0.3.0), nortest, dplyr, stringr, tidyselect,
tidyr, purrr, survival, mice, officer, flextable, rlang,
Suggests: knitr, finalfit, lme4, TH.data, rmarkdown, survminer, asaur
Packaged: 2022-01-08 08:33:46 UTC; cardiomoon

Imports: moonBook(>= 0.3.0), nortest, dplyr, crayon, stringr, tidyr,
purrr, survival, mice, officer, flextable, rlang, patchwork,
ggplot2, boot, broom, tidycmprsk, scales, maxstat, pammtools
Suggests: knitr, finalfit, lme4, TH.data, rmarkdown, survminer, asaur,
Packaged: 2022-04-05 06:04:35 UTC; cardiomoon

Title: Performs Specification Search in Structural Equation Models
Description: Implements multiple heuristic search algorithms for
automatically creating structural equation models.
Packaged: 2016-05-21 15:35:13 UTC; RJacobucci

Title: Automatic Model Selection and Prediction for Univariate Time
Description: Offers a set of functions to easily make predictions for univariate time series.
'autoTS' is a wrapper of existing functions of the 'forecast' and 'prophet' packages,
harmonising their outputs in tidy dataframes and using default values for each.
The core function getBestModel() allows the user to effortlessly benchmark seven
algorithms along with a bagged estimator to identify which one performs the best
Imports: rlang, prophet, dplyr, magrittr, lubridate, tidyr, forecast,

Packaged: 2020-06-05 11:47:28 UTC; vivienroussez

Authors@R: person("Ethan", "Ancell", email = "ethanancell@gmail.com", role = c("aut", "cre"))
Description: A modified version of the classification and regression tree (CART)
algorithm for modelling spatial data that features coordinate information.
Coordinate information can be used to evaluate measures of spatial
autocorrelation and spatial compactness during the splitting phase of the
tree, leading to better predictions and more physically realistic predictions
on these types of datasets. These methods are described in Ancell and Bean (2021)