soumyaray

# Unstandardizes construct scores of an estimated SEMinR model
# (Based on rescale function of plspm)
# usage:
#   my_model <- estimate_pls(...)
#   ustd <- unstandardize(my_model)
unstandardize_scores <- function(pls_model) {
  construct_names <- seminr:::construct_names(pls_model$smMatrix)

  construct_names -> .
    sapply(., seminr:::items_of_construct, model=pls_model) -> .

soumyaray

library(seminr)

comp1_items <- multi_items("IMAG", 1:5)
comp2_items <- multi_items("CUEX", 1:3)
comp3_items <- multi_items("CUSA", 1:3)

mobi_mm <- constructs(
  # Three first-order composites
  composite("Comp1",        comp1_items),
  composite("Comp2",        comp2_items),

soumyaray

library(seminr)

image_items <- multi_items("IMAG", 1:5)
exp_items <- multi_items("CUEX", 1:3)

items <- c(image_items, exp_items)

almost_items <-  items[-1]
  
eca_mm <- constructs(

soumyaray

# plot_regr_rsq(points) plots points and regression
#
# You can supply the following parameter:
#   points - dataframe of x,y points to plot
plot_regr_rsq <- function(points) {
  if (nrow(points) == 0) {
    plot(NA, xlim=c(-5,50), ylim=c(-5,50), xlab="x", ylab="y")
    return()
  }
  plot(points, xlim=c(-5,50), ylim=c(-5,50), pch=19, cex=2, col="gray")

soumyaray

# OOP
module Scrambler
  def scramble
    json_output = to_json
    json_output.chars.sample(json_output.length).join
  end
end

class Student
  include Scrambler

soumyaray

docs <- c("OECD", "GDPR", "FTC")

# Functions to create:
pdf_to_df <- function(doc_name) {
  filename <- paste(doc_name, ".txt")
  #...return a dataframe
  data.frame()
}

create_bigrams <- function(doc_df) { data.frame() }

soumyaray

base_url = "http://api.githbub.com/v1"

def commits_history
  repo_username = "leoli0320"
  repo_name = "my_fav_repo"

  api_endpoint = [base_url, repo_username, repo_name, 'commits'].join('/')

  req_params = {}

soumyaray

# Visualize the confidence intervals of samples drawn from a population
#   e.g.,
#     visualize_sample_ci(sample_size=300, distr_func=rnorm, mean=50, sd=10)
#     visualize_sample_ci(sample_size=300, distr_func=runif, min=17, max=35)
visualize_sample_ci <- function(num_samples = 100, sample_size = 100, 
                                pop_size=10000, distr_func=rnorm, ...) {
  # Simulate a large population
  population_data <- distr_func(pop_size, ...)
  pop_mean <- mean(population_data)
  pop_sd <- sd(population_data)

soumyaray

require 'dry-monads'
include Dry::Monads::Maybe::Mixin

soumyaray

## Original method
# def type_of_result(search_type, search)
#   search_hash = {}
#   search.each do |key, song|
#     case search_type
#     when 'tracks' then search_hash[song.track_name] = key
#     when 'artists' then search_hash[song.track_name] = song.artist_name
#     when 'albums' then search_hash[song.track_name] = song.album_name
#     when 'links' then search_hash[song.track_name] = song.track_link
#     when 'images' then search_hash[song.track_name] = song.imgs