marcosci

library(LaCroixColoR)
library(sf)
library(fasterize)
library(rayshader)
library(raster)
library(exactextractr)
library(rayrender)

# load raster into R
elevation <- raster("Olympus_Mons_ortho-image.tif")

schochastics

library(shiny)
xy <- c(784,479) #output of grDevices::dev.size("px")
url <- "https://www.youtube.com/watch?v=Ef2jmf2vy00" #copy yt link here
url <- gsub("watch\\?v=","embed/",url)
ui <- fluidPage(
  HTML(paste0('<iframe width="',xy[1],'" height="',xy[2],'" src="',url,'" frameborder="0"></iframe>'))
)
server <- function(input, output, session) {
}

KMarkert

import numpy as np
import pandas as pd

def sklearn_tree_to_ee_string(estimator, feature_names):

    # extract out the information need to build the tree string
    n_nodes = estimator.tree_.node_count
    children_left = estimator.tree_.children_left
    children_right = estimator.tree_.children_right
    feature_idx = estimator.tree_.feature

laurajanegraham

library(tidyverse)

mod_full <- lm(mpg ~ cyl + disp + hp + drat + wt + qsec, data = mtcars)
mod_g1 <- lm(mpg ~ cyl + disp + hp, data = mtcars)
mod_g2 <- lm(mpg ~ drat + wt + qsec, data = mtcars)

dat <- data.frame(full_r2 = summary(mod_full)$r.square, 
                     g1_r2 = summary(mod_g1)$r.square, 
                     g2_r2 = summary(mod_g2)$r.square) %>% 
  mutate(g1_ind = full_r2 - g2_r2, 

rafapereirabr

library(stringr)
library(spdep)
library(rgdal)
library(magrittr)
library(ggplot2)
library(sf)



#======================================================

cossatot

<!DOCTYPE qgis_style>
<qgis_style version="0">
  <symbols/>
  <colorramps>
    <colorramp type="gradient" name="Magma">
      <prop k="color1" v="0,0,3,255"/>
      <prop k="color2" v="251,252,191,255"/>
      <prop k="stops" v="0.04;4,4,21,255:0.08;14,10,42,255:0.12;26,16,65,255:0.16;40,17,89,255:0.20;57,15,110,255:0.23;74,16,121,255:0.27;90,21,126,255:0.31;105,28,128,255:0.35;121,34,129,255:0.39;137,40,129,255:0.43;153,45,127,255:0.47;169,50,124,255:0.51;185,55,120,255:0.55;202,62,114,255:0.59;217,70,106,255:0.62;230,81,98,255:0.66;240,96,93,255:0.70;247,113,91,255:0.74;251,132,96,255:0.78;253,151,104,255:0.82;254,170,116,255:0.86;254,188,130,255:0.90;253,207,146,255:0.94;253,225,163,255:0.98;252,243,181,255"/>
    </colorramp>
    <colorramp type="gradient" name="Inferno">

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

rasmusab

# Test of Significance, takes the same arguments as t.test() .
signif.test <- function(x, ...) {
  p <- t.test(x, ...)$p.value
  
  # List of p excuses retrieved from http://mchankins.wordpress.com/2013/04/21/still-not-significant-2/
  p_excuses <- c(
    "(barely) not statistically significant <p>",
    "a barely detectable statistically significant difference <p>",
    "a borderline significant trend <p>",
    "a certain trend toward significance <p>",

casallas

Package	Ordinal	Multinomial	Partial proportional odds	Scale Effects	Random Effects
nnet	no	multinom	N/A	N/A	No
MASS	polr	No	No	No	No
ordinal	clm/clm2	all X's in nominal (may not converge)	offending X's in nominal	via scale	clmm/clmm2
VGAM	yes	?	?	?	No
MCMCglmm	yes	?	?	?	?
Mixcat	yes	?	?		npmlt
mlogit	?	mlogit	N/A	N/