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SteveBronder / debug.stan
Created March 12, 2020 18:21
data {
int<lower = 1> nSubjects;
int nIIV;
}
parameters {
cholesky_factor_corr[nIIV] L;
matrix[nIIV, nSubjects] etaStd;
}
@SteveBronder
SteveBronder / ben_test.cpp
Last active March 6, 2020 04:34
#include <stan/math/prim/core.hpp>
#include <stan/math.hpp>
#include <gtest/gtest.h>
#include <algorithm>
#include <sstream>
#include <tuple>
#include <vector>
std::ostream* msgs = nullptr;
@SteveBronder
SteveBronder / move_vs_copy_bench.csv
Created March 3, 2020 16:43
bench time cpu iters type test
add_copy_bench/2_mean 187 ns 187 ns 35 mean copy
add_copy_bench/2_median 186 ns 186 ns 35 median copy
add_copy_bench/2_stddev 3.10 ns 3.09 ns 35 stddev copy
add_copy_bench/4_mean 335 ns 334 ns 35 mean copy
add_copy_bench/4_median 333 ns 333 ns 35 median copy
add_copy_bench/4_stddev 3.46 ns 3.46 ns 35 stddev copy
add_copy_bench/8_mean 1159 ns 1159 ns 35 mean copy
add_copy_bench/8_median 1156 ns 1156 ns 35 median copy
add_copy_bench/8_stddev 9.47 ns 9.47 ns 35 stddev copy
@SteveBronder
SteveBronder / bad_single_index.hpp
Last active February 28, 2020 04:36
// Code generated by stanc acfb4612
#include <stan/model/model_header.hpp>
namespace example_model_namespace {
template <typename T, typename S>
std::vector<T> resize_to_match__(std::vector<T> &dst,
const std::vector<S> &src) {
dst.resize(src.size());
return dst;
@SteveBronder
SteveBronder / bad_assign_operator.hpp
Created February 25, 2020 22:58
// Code generated by stanc acfb4612
#include <stan/model/model_header.hpp>
namespace blah_model_namespace {
template <typename T, typename S>
std::vector<T> resize_to_match__(std::vector<T> &dst,
const std::vector<S> &src) {
dst.resize(src.size());
return dst;
@SteveBronder
SteveBronder / benford_signif_patch.R
Last active January 24, 2020 21:34
test_x = runif(10000, min = 0, 1000)
test_x = test_x * 1e+10
orig_signif = function(x, digits = 1) {
xx = abs(x);
return(trunc((10^((floor(log10(x)) * -1) + digits - 1)) * x))
}
new_signif = function(x, digits = 1) {
xx = abs(x);
@SteveBronder
SteveBronder / ex_brm.stan
Last active January 13, 2020 07:27
// generated with brms 2.10.3
functions {
}
data {
int<lower=1> N; // number of observations
vector[N] Y; // response variable
// data needed for ARMA correlations
int<lower=0> Kar; // AR order
int<lower=0> Kma; // MA order
// number of lags per observation
@SteveBronder
SteveBronder / transform_data_nogpu.hpp
Created January 10, 2020 16:46
// Code generated by Stan version 2.21.0
#include <stan/model/model_header.hpp>
namespace test_mod1_model_namespace {
using std::istream;
using std::string;
using std::stringstream;
using std::vector;
@SteveBronder
SteveBronder / large_mod.R
Last active December 22, 2019 23:24
library(brms)
library(cmdstanr)
make_fake_df = function() {
N = 100000
foo_groups = apply(combn(LETTERS, m = 3), 2, function(x) paste(x, collapse=''))
test_df = data.frame(
x1 = runif(N, -2, 2),
x2 = runif(N,-2 , 2),
x3 = runif(N, -2, 2),
@SteveBronder
SteveBronder / parallel_pic.tex
Last active December 22, 2019 18:22
\documentclass[border=10pt]{article}
%%%<
\usepackage{verbatim}
%%%>
\usepackage{tikz}
\begin{document}
Given Data $x_i \in X$, $y_i \in Y$ and parameters $\alpha$, $\beta$ we can break the operations up into N groups (3 in example below).
\\