## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  warning = FALSE,
  message = FALSE
)

## -----------------------------------------------------------------------------
library(PsyML)

## -----------------------------------------------------------------------------
set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))

sup <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
sup

## -----------------------------------------------------------------------------
set.seed(1)
X <- data.frame(a = c(rnorm(40), rnorm(40, 3)),
                b = c(rnorm(40), rnorm(40, 3)),
                c = rnorm(80))

uns <- unsupervised_ml(X,
                       clustering_algorithms = c("kmeans", "hierarchical"),
                       k_range = 2:4, reduction_methods = "pca",
                       anomaly_methods = "mahalanobis",
                       run_eda = FALSE, seed = 1, verbose = FALSE)
uns$evaluation$comparison

## -----------------------------------------------------------------------------
set.seed(1)
n <- 300
dc <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
dc$treat <- rbinom(n, 1, plogis(dc$x1))
dc$y <- 1.5 * dc$treat + dc$x1 + 0.5 * dc$x2 + rnorm(n)

cau <- causal_ml(dc, treatment = "treat", outcome = "y",
                 methods = c("propensity", "meta_learners"),
                 ps_methods = c("ipw", "doubly_robust"),
                 meta_learners = "T", run_eda = FALSE,
                 seed = 1, verbose = FALSE)
cau$summary_table