sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
aa1b5b9f0a85e8beaf174870cc4dd06ad38ef2eadbb495934abb433ac7923298 | Shell | 23,295 | 446 | #! /bin/bash
# ============================================================
# pipeline script for processing fastq to gvcf
# ============================================================
# get command line options
# ============================================================
args=`getopt -o "r:,d:,t:,o:,s:" -l "reso... |
9f2bc1e0dd1c3fc636d7fb6ba9c660c86b38feeab86eb67563d49315264ae2a1 | Shell | 24,073 | 368 | #!/bin/bash
#SBATCH -c 1
#SBATCH -p normal
#SBATCH --mem=64g
#SBATCH --time=12:00:00
sample=$1
organism=$2
reference_path=$3
scriptFolder=$4
echo $sample
echo $organism
echo
date
cd $5
# v6.1: bug fix and vastly improved method for generation of circRNA_exon_usage.txt
# v7.0: Added the script previously called ext... |
2270ff2b4f10f981166ee4f672dadc80006de402a22385eb56883cbd27aee0e5 | Shell | 24,824 | 546 | #!/bin/bash
# 'KAS-Analyzer R-loop' was developed by Ruitu Lyu on 12-22-2021.
# Stop on error
set -e
# help arguments
usageHelp="Usage: KAS-Analyzer R-loop [ -h/--help ] [ -t threads ] [ -o prefix ] [ -s assembly id ] [ -p peaks ] [ -b bin size ] [ -f fold change ] [ -l labels ] [ -n Input ] [ -k spKAS-seq ]"
example... |
d2060b972fa0c9255010f3561f4a48f548f61e2d384f85e42e20820d95ded22a | Shell | 26,060 | 636 | #!/usr/bin/env bash
set -euo pipefail
# ============================================================
# OmicVerse Guided Installer
# Supports: macOS, Linux, Windows (WSL / Git Bash)
# Python 3.10 / 3.11
# ============================================================
# ── colour / formatting ───────────────... |
823cca2c1628d428081fd3e1e92cd2fd2934c885dd6e082437c000131f84a460 | Shell | 26,194 | 347 | #!/bin/bash
#SBATCH -c 8
#SBATCH -p normal
#SBATCH --mem=128g
#SBATCH --time=72:00:00
# This is version 5.5 of the Nanopore circRNA dataction pipeline
# This version uses blat to map reads. NanoFilt is used to trim low quality reads (q 7). Parallel BLAT is used to map nanopore reads to genome.
data_folder=$1
sampl... |
fd6d690749d74e8f4b108ac7a006f2961244c89b049d15cb62f16d146b90aeae | Shell | 26,370 | 659 | ################################################################################################
## Define functions
# 1) run_CCS <input_ccs_bam> <prefix_output_name>
# 2) run_LIMA <sample> <"no_multiplex"/"multiplex">
# 3) run_REFINE <sample>
# 4) run_targeted_REFINE <sample> <input_config_file>
# 5) merging_at_ref... |
4b716706c3ec7552ebb20c9c1724557ca3154bf9281c28d8060b731ae414fdb8 | Shell | 26,838 | 645 | #!/bin/bash
set -Exeuo pipefail
RED='\033[0;31m'
BOLD_RED="\033[1;31m"
GREEN='\033[0;32m'
MAGENTA='\033[0;35m'
CYAN='\033[0;36m'
NC='\033[0m' # No Color
log_info() {
echo -e "[$(date +'%Y-%m-%d %H:%M:%S')] ${CYAN}$1${NC}" | tee -a "${gather_batch_evidence_stdout}"
}
log_success() {
echo -e "[$(date +'%Y-%m-%d %... |
720eb49ed7ebdf5495d975a525d120e1b99260ba47bb136714e56a8f335ca4e7 | Shell | 27,800 | 561 | #!/bin/bash
# The "qmri-ald" program provides automated image-processing pipelines for
# structural MRI images primarily acquired in pediatric healthy controls
# and pediatric patients with cerebral adrenoleukodystrophy. The program
# provides preprocessing, processing, quantitative, and statistical analysis
# of MRI i... |
87dfcadcf52ea96774176819e79cf3f2ae13eb29030a0f025c49a53d85648bc4 | Shell | 28,223 | 214 |
qsub -P trapnelllab -l mfree=10G -wd /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_07_19_largeScaleMixedModeling_nobackup/ /net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_07_19_largeScaleMixedModeling_nobackup/singleMMrun.sh Endocardium
qsub -P trapnelllab -l mfree=10G -wd /net/trapnell/... |
b65f1e9337f4001d3776c904af3d33aef5f2c3f8c8b8f7f2b52bb809cefda090 | Shell | 29,755 | 710 | #!/bin/bash
###########################################################################################
##### Correcting the demo xlsx
### HCgroup
## echo the sub ID
for sub in $(cat /media/data2/Epilepsy_Shape/List/HC_recon_files.txt);
do
name=$( echo ${sub%%_*} )
echo sub-HC${name:11:1}
done
## echo the sesi... |
d544b8e1d5f18d09bbeec428ee0f535ba9725b1e92c3fa50fa7465c6b5c7f23a | Shell | 30,801 | 583 | #!/bin/bash
# 'KAS-Analyzer diff' was developed by Ruitu Lyu on 1-20-2022.
# Stop on error
set -e
# help arguments
usageHelp="Usage: KAS-Analyzer diff [ -h/--help ] [ -t threads ] [ -o prefix ] [ -s assembly id ] [ -r regions ] [ -p peaks ] [ -f fold change ] [ -c comparison file ] [ -l labels ] [ -k KAS-seq ] "
exam... |
016c576ed8413703a6c53e2b1907403c28189a022e5d750ee1a4d100c906d263 | Shell | 31,551 | 616 | #!/bin/bash
# 'KAS-Analyzer TC' was developed by Ruitu Lyu on 1-19-2022.
# Stop on error
set -e
# help arguments
usageHelp="Usage: KAS-Analyzer TC [ -h/--help ] [ -t threads ] [ -n ratios ] [ -o prefix ] [ -g assembly id ] [ -r regions ] [ -s bin size ] [ -b ] [ -p peaks ] [ -d differential analysis ] [ -a annotation... |
0fb4d78c1ce0506fef28cb6a663e4fdfb004d77627550ed395198ed8fa505425 | Shell | 32,559 | 638 | #!/bin/bash
# 'KAS-Analyzer diff' was developed by Ruitu Lyu on 1-20-2022.
# Stop on error
set -e
# help arguments
usageHelp="Usage: KAS-Analyzer diff [ -h/--help ] [ -t threads ] [ -o prefix ] [ -s assembly id ] [ -r regions ] [ -p peaks ] [ -f fold change ] [ -c comparison file ] [ -l labels ] [ -k KAS-seq ] "
exam... |
ec0254f6a6bcd3c93cff7a1679f110aae4cd7bb66e220e360c8b26cb46544366 | Shell | 33,250 | 494 |
#----------------------------------------------------------------
# 0.1 personal bismark database prepration #
#----------------------------------------------------------------
##building index
for sample in DB2 DS2 LB2 LS2
do
sbatch --export=sample="$sample" --output=/mnt/home/quanjian/... |
ea0559ed716518221e6d3a6f87a0864f6512c1e6c510a4c5ec35c6611113f6d1 | Shell | 34,622 | 758 | #!/bin/bash
# 'KAS-Analyzer KAS-seq' was developped by Ruitu Lyu on 12-08-2021.
# Stop on error
set -e
## Read arguments
usageHelp="Usage: KAS-Analyzer KAS-seq [ -h ] [ -a aligner ] [ -t threads ] [ -i index path ] [ -u ] [ -e extend length ] [ -o prefix ] [ -s ass... |
6024f28d11ee823611b715cc239766db13af0a3c1fcac209d4beb3c91da05e99 | Shell | 36,329 | 759 | #!/bin/bash
# 'KAS-Analyzer spKAS-seq' was developped by Ruitu Lyu on 12-08-2021.
# Stop on error
set -e
## Read arguments
usageHelp="Usage: KAS-Analyzer spKAS-seq [ -h ] [ -t threads ] [ -i index path ] [ -u ] [ -r ] [ -f fold change ] [ -b bin size ] [ -e extend ... |
a9191e6a3f3e8052518e8fbdde1a6023c3a1ed9fce32569a9f4269886e114be4 | Shell | 38,646 | 658 | #!/bin/bash
# The "qmri-ald" program provides automated image-processing pipelines for
# structural MRI images primarily acquired in pediatric healthy controls
# and pediatric patients with cerebral adrenoleukodystrophy. The program
# provides preprocessing, processing, quantitative, and statistical analysis
# of MRI i... |
ca026022ee564328e72570010f778a3769a9dc8acf26e13ff64cb95921de93d4 | Shell | 40,105 | 853 | ## Figure 1A
for hemi in L R; do
vol=/vols/Scratch/neichert/hipmac/sub-40_x_vol_${hemi}.nii.gz
#mysurf=/vols/Scratch/neichert/hipmac/output/sub-40/surf/sub-40_hemi-${hemi}_space-corobl_den-0p5mm_label-hipp_midthickness.surf.gii
mysurf=/vols/Scratch/neichert/hipmac/output/sub-40/surf/sub-40_hemi-${hemi}_spac... |
dc57d1f5a746cda09c435f5d570161d748259385e6b1d98e58af55c74bbbbd69 | Shell | 40,572 | 655 | #!/bin/bash
# 'KAS-Analyzer heatmap' was developed by Ruitu Lyu on 12-14-2021.
# Stop on error
set -e
# help arguments
usageHelp="Usage: KAS-Analyzer heatmap [ -h/--help ] [ -t threads ] [ -e length ] [ -s assembly id ] [ -q ] [ -u samples using ] [ -m maximum value ] [ -o prefix ] [ -r regions ] [ -p peaks ] [ -l la... |
60c49146d71da98b019605264603b1fe11e3f29c15164cac604fc68c3f270fd6 | Shell | 40,813 | 159 | GPUS=${GPUS:-8}
# RAW domain
./tools/dist_test.sh configs/detection/ruod_dataset/atss_r50_fpn_1x_ruod.py work_dirs/ruod/base/atss_r50_fpn_1x_ruod/epoch_12.pth $GPUS --cfg-options test_evaluator.outfile_prefix=work_dirs/urpc2020/base/atss_r50_fpn_1x_urpc/atss_result
./tools/dist_test.sh... |
97e2705f8ab127a7e30b4e34c61024a95a807516f64ff5307f86c0f8eb2cdcf7 | Shell | 49,619 | 159 | GPUS=${GPUS:-8}
# RAW domain
./tools/dist_train.sh configs/detection/ruod_dataset/atss_r50_fpn_1x_ruod.py $GPUS --work-dir work_dirs/ruod/base/atss_r50_fpn_1x_ruod --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 randomness.seed=0
./tools/dist_train.sh configs/detection/ruod_datas... |
5533fc9ac8e43da4ade7070bb85221b9a9aff77ef3523429367276e9648a57cc | Shell | 56,845 | 407 |
# 11-8-21: Run models using age on a linear scale. Using a log scale immediately raise eyebrows when I spoke w/ Jennifer,
# and probably will be a thorn in my side to justify to reviewers. If they ask to compare likelihoods/errors I can, but
# probably simplest to just analyze based on linear age
... |
4d426046ef243f22b7a22d6840e7d6f4dc2d01892cec8a34a8a6886e54a34a49 | Shell | 58,847 | 1,358 | #!/bin/bash
# For details: https://serverfault.com/a/103569
# saves original stdout to &3 and original stderr to &4
# without this, the logs of subprocess can get mixed with the parent's logs.
exec 3>&1 4>&2
RED='\033[0;31m'
BOLD_RED="\033[1;31m"
GREEN='\033[0;32m'
MAGENTA='\033[0;35m'
YELLOW='\033[0;33m'
CYAN='\033[... |
9df1248e0d344112480bbf9bf2db84465aea23d1f6ffcf98d3dd3c4eccc265d7 | Shell | 69,750 | 187 | GPUS=${GPUS:-8}
## RAW domain
./tools/dist_train.sh configs/detection/duo_dataset/atss_r50_fpn_1x_urpc-coco.py $GPUS --work-dir work_dirs/urpc2020/base/atss_r50_fpn_1x_urpc --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 randomness.seed=0 val_evaluator.outfile_prefi... |
22b4a4d805b700265438f519728ad38bfb40f18d62904961203d3581271ef64f | Stan | 322 | 23 | data {
int<lower=0> N;
int<lower=0> M;
int<lower=0> C;
array[N] int exposure;
matrix[N,C] X;
}
parameters {
matrix[C, M] beta;
}
generated quantities{
array[N, M] int counts;
matrix[N, M] alpha = (X * beta);
for(n in 1:N)
counts[n] = multinomial_rng(dirichlet_rng(to_vector(alpha[n])), exposure[n]) ;
... |
6c6741718bc58b74980e0b5cf01e76b6cfc344fc1f5d76ab7e564839a4731174 | Stan | 636 | 29 | data {
int<lower=0> N; // number of data points
int<lower=1> D; // number of dimensions
int<lower=1> K; // number of clusters
vector[D] y[N]; // observations
}
transformed data {
real<upper=0> neg_log_K;
neg_log_K = -log(K);
}
parameters {
vector[D] mu[K]; // cluster means
}
transformed parameters {
... |
6717d870b1aaa457abf48c518c223b66a817f14f62efabea9f4fadd6efcd01b2 | Stan | 1,105 | 49 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
2996365fa49f653d999ab4814c5beb712b639b5598d4d908e8e8081c96f9b420 | Stan | 1,196 | 42 | data{
int N; // Number of subjects
int M; // Number of categories
int C;
int A;
array[N] int exposure;
matrix[N, C] X;
matrix[A, A] XA;
matrix[C,M] beta;
real variability_multiplier;
}
parameters{
array[2] real prec_coeff;
real<lower=0> prec_sd;
}
generated quantities{
array[N, M] int counts;
... |
939027a5762e159415fadc06c7b87a78fc86e6e7007c0cf4df327378322b2ac5 | Stan | 1,500 | 56 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
42d71885442a44d01f371cf8d69e5b6506c9c2ceba9cda775de2b0895da6d0db | Stan | 1,557 | 80 | functions{
real dirichlet_multinomial2_lpmf(array[] int y, vector alpha) {
real alpha_plus = sum(alpha);
return lgamma(alpha_plus) + sum(lgamma(alpha + to_vector(y)))
- lgamma(alpha_plus+sum(y)) - sum(lgamma(alpha));
}
matrix vector_array_to_matrix(array[] vector x) {
matrix[size(x)... |
73b4980c195ed8142c4124bae3bb66b36b9c36ddcbc0535ac8b3d3ab1b46b484 | Stan | 1,687 | 83 | functions{
array[] int dirichlet_multinomial2_rng(vector alpha, int exposure) {
return multinomial_rng(dirichlet_rng(alpha), exposure);
}
matrix vector_array_to_matrix(array[] vector x) {
matrix[size(x), rows(x[1])] y;
for (m in 1:size(x))
y[m] = x[m]';
return y;
}
}
data{
int N;
int M;
int C;
... |
67898685b4cbff51fa67b6d9feb7d9666c28596869daf8a1b3e721104a1e777f | Stan | 1,815 | 66 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
94b1a092aadbb34042c6c9aaf98e0e4d8dc519436eb50e13fe553d7899da3605 | Stan | 1,875 | 91 | functions{
real dirichlet_multinomial2_lpmf(array[] int y, vector alpha) {
real alpha_plus = sum(alpha);
return lgamma(alpha_plus) + sum(lgamma(alpha + to_vector(y)))
- lgamma(alpha_plus+sum(y)) - sum(lgamma(alpha));
}
matrix vector_array_to_matrix(array[] vector x) {
matrix[size(x), ... |
319dacc7a74f291af49b31f5620962abc759734756121454329ac1b6c8e86bd5 | Stan | 1,932 | 72 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
634301712b75893b1dccd03339a76b94e878fc962f9ee315f4918d612a46f563 | Stan | 2,000 | 89 | functions{
real dirichlet_multinomial2_lpmf(array[] int y, vector alpha) {
real alpha_plus = sum(alpha);
return lgamma(alpha_plus) + sum(lgamma(alpha + to_vector(y)))
- lgamma(alpha_plus+sum(y)) - sum(lgamma(alpha));
}
matrix vector_array_to_matrix(array[] vector x) {
matrix[size(x), ... |
70fb26b56a72ee3b716a9d0d1d9da4245f763cc6c4884debd13eb42fa8439994 | Stan | 2,391 | 77 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
8d81aa720186edc9642a19d176beb19db115857e28ad06268b5c2cf799168415 | Stan | 2,604 | 85 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
1d8bc6d70f5fa2c4fb7a3b33772a95e95a7526b208ef2bad722b4e463f891eaf | Stan | 2,792 | 90 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
5b534daedcbf1ca3ae74f79c3dba4fd35ca0b52d821199abee681aef1620d09d | Stan | 2,835 | 86 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
8123663e6274d84cdc71db285f375850debb42e8c9df6f34e62a6c3759c0886a | Stan | 3,019 | 115 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
fb97bf7d7ca3305eea9d4968f4a0209e2a0c1be9bc397e5d2dd9899756c8f30d | Stan | 3,089 | 91 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
98dcdd4b8ce2b1fcc02c7eae23a3c1b42e86418d3c6a7e70ae9d533a862366dd | Stan | 3,150 | 96 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
e11551ba1f54a0395b1e45ece5d1da869084b02882004db33a1bcab4f3ded0b1 | Stan | 3,267 | 135 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_l, vector vars, vector nu) {
real out;
out = student_t_lpdf(resp | nu, mu_u*cu + mu_l*cl, sqrt(vars));
return out;
}
vector color_rng(vector cu, vector cl, matrix mu_u,
matrix mu_l, vector vars, vecto... |
99b935bfdb533740eb8249df91654233ddb89b90eb14a29a83ca9e6691a37287 | Stan | 3,353 | 98 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
30e15445e4a15a3228d49c8d7332a4db8c13eb826cb86986d3eca1746a48a7a8 | Stan | 3,422 | 139 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_l, vector inter,
vector vars, vector nu) {
real out;
out = student_t_lpdf(resp | nu, mu_u*cu + mu_l*cl + inter, sqrt(vars));
return out;
}
vector color_rng(vector cu, vector cl, matrix mu_u,
matri... |
b7404303282b3144417582f884e48e2fbf55789ae61ddccc8bfe9a3fc1830c2a | Stan | 3,514 | 93 | data{
int N_simulated; // Number of subjects
int M_simulated; // Number of categories
int C_simulated;
int A_simulated;
array[N_simulated] int exposure_simulated;
matrix[N_simulated, C_simulated] X_simulated;
matrix[A_simulated, A_simulated] XA_simulated;
matrix[C_simulated,M_simulated] beta_simulated;
... |
6619d3b909ba7a3ba484623067e2bc234e73eb5753a2bdb24cc0de378849270c | Stan | 3,609 | 126 | data {
//sizes
int<lower=0> T; // number of trials
int<lower=1> S; // number of runs
real<lower=0> report_var_var_mean;
real<lower=0> report_var_var_var;
real<lower=0> report_var_mean_mean;
real<lower=0> report_var_mean_var;
real<lower=0> swap_weight_var_mean;
real<lower=0> swap_weight_var_var;
rea... |
f643098754567d951d748520dda84239152a63480ae00becaf9d6c28fb804cfc | Stan | 3,627 | 126 | data {
//sizes
int<lower=0> T; // number of trials
int<lower=1> S; // number of runs
real<lower=0> report_var_var_mean;
real<lower=0> report_var_var_var;
real<lower=0> report_var_mean_mean;
real<lower=0> report_var_mean_var;
real<lower=0> swap_weight_var_mean;
real<lower=0> swap_weight_var_var;
rea... |
e9bed91416c9420831d0e0c17eaa5dd1f727cd36e1e9064486ec72ec6244f25e | Stan | 3,743 | 142 | functions{
vector Q_sum_to_zero_QR(int N) {
vector [2*N] Q_r;
for(i in 1:N) {
Q_r[i] = -sqrt((N-i)/(N-i+1.0));
Q_r[i+N] = inv_sqrt((N-i) * (N-i+1));
}
return Q_r;
}
row_vector sum_to_zero_QR(row_vector x_raw, vector Q_r) {
int N = num_elements(x_raw) + 1;
row_vector [N] x;
... |
bf2c38625fbaf99aefb96f55de094c48192041bbdcc05a64455e01dc5ddd7164 | Stan | 4,352 | 126 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
f82272b86a72aef6d6130bd8ead9f07b43fad1034af80216c05d19e54bdd71b3 | Stan | 4,641 | 175 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_l, vector vars, vector nu) {
real out;
out = student_t_lpdf(resp | nu, mu_u*cu + mu_l*cl, sqrt(vars));
return out;
}
vector color_rng(vector cu, vector cl, matrix mu_u,
matrix mu_l, vector vars, vecto... |
8b565b5f9d39c7da630e30c9f1497bc5bd130c43b0dca4182f6e2c5d581c5151 | Stan | 4,686 | 176 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_l, vector inter,
vector vars, vector nu) {
real out;
out = student_t_lpdf(resp | nu, mu_u*cu + mu_l*cl + inter, sqrt(vars));
return out;
}
vector color_rng(vector cu, vector cl, matrix mu_u,
matrix... |
d2637d540679db9b9675fb85bc1700d9c47d1f960f703f01f95e1491daab50cd | Stan | 4,686 | 136 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
245444770ca0e234351f5573ef031d7acb758305195524351ffbd88510529720 | Stan | 5,638 | 165 | data {
int<lower=0> T; // number of trials
int<lower=1> N; // number of neurons
int<lower=1> K; // number of color
vector[N] y[T]; // neural activity
int<lower=0,upper=1> cue[T]; // upper or lower indicator
vector[K] C_u[T]; // upper color, 2-hot simplex vector
vector[K] C_l[T]; // lower color, 2-hot... |
81cd26dd9312a158d47783dedcea902d715125c898d42e996a6ef6129b395274 | Stan | 6,245 | 153 | array[] matrix reshape_to_3d_matrix(
int M,
int n_groups,
int how_many_factors_in_random_design,
matrix input_matrix,
array[,] int group_factor_indexes_for_covariance
) {
// PIVOT WIDER
// increase of one dimension array[cell_type] matrix[group, factor]
array[M] m... |
571dbb113d19a8d96f60f3faf68a73f33ba00ac5724570f8707ca8b2fe552afa | Stan | 7,967 | 276 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_d_u,
matrix mu_l, matrix mu_d_l,
int cue,
vector vars, vector nu,
vector intercept_up, vector intercept_down) {
real out;
out = (cue*(student_t_lpdf(resp | nu, mu_u*cu + mu_d_l*cl + intercept_up,
... |
eea4046a8b853b67cf0a3d6408f8574fc4e2b805628f07cdd9b3d88d558a09b2 | Stan | 8,640 | 289 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_d_u,
matrix mu_l, matrix mu_d_l,
int cue, vector vars,
vector nu, vector intercept_up, vector intercept_down) {
real out;
out = (cue*(student_t_lpdf(resp | nu, mu_u*cu + mu_d_l*cl + intercept_up,
... |
2d7845420772c28780934d950c16f73de6339ad49d06e1f978b68c680396dce3 | Stan | 9,563 | 321 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_d_u,
matrix mu_l, matrix mu_d_l,
int cue, vector vars,
vector nu, vector intercept_up, vector intercept_down) {
real out;
out = (cue*(student_t_lpdf(resp | nu, mu_u*cu + mu_d_l*cl + intercept_up,
... |
d7075a946cca9a494fed0e388ef846c183f59d941cc4cf7e6febae4e2e3bfb9f | Stan | 10,610 | 359 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_d_u,
matrix mu_l, matrix mu_d_l,
int cue, vector vars,
vector nu, vector intercept_up, vector intercept_down) {
real out;
out = (cue*(student_t_lpdf(resp | nu, mu_u*cu + mu_d_l*cl + intercept_up,
... |
dd10726d042293706e106f3d0c0973c4f08572a6240ef501229eb1d1d86a5b72 | Stan | 10,610 | 359 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_d_u,
matrix mu_l, matrix mu_d_l,
int cue, vector vars,
vector nu, vector intercept_up, vector intercept_down) {
real out;
out = (cue*(student_t_lpdf(resp | nu, mu_u*cu + mu_d_l*cl + intercept_up,
... |
067d9140423fd9bdb677e286210d9179215fecb45f500904149fa330b4f8adb0 | Stan | 11,314 | 388 | functions {
real color_likelihood(vector resp, vector cu, vector cl,
matrix mu_u, matrix mu_d_u,
matrix mu_l, matrix mu_d_l,
int cue, vector vars,
vector nu, vector intercept_up, vector intercept_down) {
real out;
out = (cue*(student_t_lpdf(resp | nu, mu_u*cu + mu_d_l*cl + intercept_up,
... |
e391f705fa27ee9f1062bc4f64e675420e929db591488737f29d25c885935f55 | Stan | 14,977 | 329 | functions{
#include common_functions.stan
// Linear predictor contributions for random-effect design blocks; mu is M × N (types × samples).
matrix add_seen_random_effect_contribution(matrix mu_mn,
matrix x_n_times_k,
... |
e59bfef5d0d580b994efbc1a9604dcf32669040d9f4c75f5899b16db986056f0 | Stan | 30,825 | 759 |
functions{
#include common_functions.stan
array[] int rep_each(array[] int x, int K) {
int N = size(x);
array[N * K] int y;
int pos = 1;
for (n in 1:N) {
for (k in 1:K) {
y[pos] = x[n];
pos += 1;
}
}
return y;
}
real abundance_variability_regression(
... |
c4815758e925a8629c048a64926f91b009898ddf1675b75cbac239bf55a73bb8 | Text | 10 | 1 | # NYUMets
|
6e92b6cd2b82e5c4d4d82250c940c44490597e776814369b84749efd76b7114e | Text | 12 | 1 | # SpikeDecay |
f617c6c40261f7846003ebc8d1ea901d272d77acb0b74ff20677249ef4a5417a | Text | 12 | 1 | # lung_brain |
7d4489d57ea0aa1cacc8234c9f7a2a8ae2967648c5753e00b9fdc270b74b36f0 | Text | 15 | 1 | # StripeRemoval |
8e7669821dfbc2cb7732bf2d200f8243c8c66b6b6c66da3a7eb9e70562925dcd | Text | 15 | 1 | # ColocGenes2D
|
401b5746210f7dcff311f7e213a64636f3760f57392662224ec1ae4704d466c9 | Text | 16 | 1 | # Genes_2DCells
|
bf36a040408e5a42857396bdb6b9005d674c6ca6b39b39a97df43ecb960c7311 | Text | 16 | 1 | # CRLM_Radiomics |
9f6e0bde19da842f5769a6e2ae8c3ea2d7c0650ebea2175d96dd1862e6318071 | Text | 18 | 1 | # EM-analysis-code |
883434ed6e9620199da9364ceb8a066e4b3491979cf438d15c1f43c6b6a12cf5 | Text | 21 | 1 | # ParabolicAvalanches |
10488f1d36601122be224afd275ed68cd68733fcd5c4f3b6eb89498d251422a0 | Text | 22 | 1 | # FreeLegTrackAnalysis |
7ad41d6fd3eab6ab08501b0b81b5e674b25f51104049b69c0f71514c4f7861b1 | Text | 23 | 1 | Codes for melanoma DT.
|
3953d2162a845b47c9944312bbfaccd0c6b34e87978d9227b1c56f65682c95a9 | Text | 25 | 1 | # bhc_fmri_least_squares
|
b67e8806694c33b25dc095d65b5455dbeaab1b84c51dd5d8cb6abf8e1931a36b | Text | 25 | 1 | # Glycerophospholipid-CCS |
49d55138ef2ef57e34945da1fd9b2c79eca9c4cfd70bbf78b78a55293c99aef1 | Text | 27 | 1 | # Park-Nature-Genetics-2024 |
01b4dc65f3165dc299fbc453565bde6484f4cf37b3f03be65838ab97c3902afa | Text | 28 | 1 | # DelineatorSwitchAndCompose |
82e701f050944169f83b04f0ff9e8a8828d5b67e5576180f3549563ed56b90de | Text | 29 | 1 | # UnderstandingLearningWithML |
7d77e17e8908de4d602c362ea41cc567e1acfcc6f75bc446e1ebd12343838300 | Text | 36 | 2 | # BlenderApp
BlenderNeuronAnalysis
|
d9c290fef3d9b712afa22cb5bace374e5ffc798026d30035a4c4e8e464c1e2df | Text | 37 | 1 | # DA-Pharmacology-ephys-analysis-2024 |
6288675a482bd3b19c28fdfb549eb2fa8d7012b4988c5e0a9607c4a5f6235912 | Text | 41 | 1 | # Thalamic_and_Cortical_Organoid_snRNASeq |
a16dfc22b0e1299d9c2f3fd93835c0584262a30e8b003fe6456df04ea82de0d1 | Text | 49 | 2 | # Bruentgens_et_al_2024
Code for nested Analysis
|
a7a3da3c90057e0578c061b78a7dc6eab5efd9e700af40d48279dc7201c48f7d | Text | 49 | 2 | # eMRISystems_HF-2
Open-Source Recording System
|
21ac7ec361b91b13397507af413ac5db4d6d05cc7a0eeb7cb8b5239cd2484f4d | Text | 59 | 2 | # Diameter
Fiji (ImageJ) macros used to measure diameters.
|
e17f8543a414746638226d064effa9d6f026560022ac037e58a974e4988f4644 | Text | 64 | 2 | # QEN
Quality evaluation network for electron microscopy images
|
aae68d41411565f9b668286bc133d2e288c0869b6523e02dda43868ad12bba53 | Text | 65 | 2 | # 2024-Chua-et-al.
2024 Chua et al. Analysis of Irregular Pulses
|
d70491f6dc0b73d10b536c9535e69e2295a89f0e7feceb5486b2f271fffd4f2d | Text | 65 | 2 | # LegTrackAnalysis
Code for analyzing leg tracking data from APT
|
df58ad347e635da06469a881c751092edc0b496bf69e4d0db06c8e9b61700647 | Text | 66 | 1 | Level Dependent Auditory Brainstem Responses to Continuous Speech
|
b8dbec3202b60875d2a3bcf9470f79f6b9be5ad5208fc8c7b17b565772c7f4b1 | Text | 68 | 2 | # baumNetCoupling
Structure-function coupling in PNC brain networks
|
82da21e38ebbda2e206f281ae81cb1fe37396041ac87f746591c5f2c17e3be5b | Text | 70 | 2 | # publications
Repository for code related to scientific publications
|
0408eb59c3bc5d100ba4b294226b7396cf3e323746ba23dd894718cf77704bbd | Text | 71 | 2 | # Thalamic_and_Cortical_Organoid_snRNASeq
DOI: 10.5281/zenodo.12559453
|
94bc4ac3d9cd46d215024e1bdbd889a32abddb4cb10c6fb5f90181cb423b6c01 | Text | 74 | 2 | # TouchTask_PD_study
Simple reach task for parkinsonian non-human primate
|
109c148cdb61ab77f18d04c73bec1b6029dcbf54b02729fafbd228a14f272660 | Text | 76 | 2 | # eNeuro-2024
Files contain the analyzed data for each of the data figures.
|
6efd7b67fe35f9de3e0999fd01f0c7bf0d61a86dee6db85755cdeaa30930bacc | Text | 77 | 2 | # RSI
Code to fit a Restriction Spectrum Imaging Model to Diffusion MRI Data
|
ff275b816c56f2b197776d8565ca51a356c3896896b586fd5b4904cdabfd1347 | Text | 78 | 2 | # CDI_Model
Deep Learning Model to Predict Outcomes of C. difficile Infection
|
968c20fae7f48f0f5a0f25890d35eb5a3a2d708b849ce723227628211862fcdb | Text | 79 | 2 | # FastMS-DBL-GNG
FastMulti-Scale Distributed Batch Learning Growing Neural Gas
|
46244971ceb797f05d510f899496a88fa8e37c9ed8818ca5d152af9afb470955 | Text | 82 | 2 | # fetal-brain
Repository with code for our work related to fetal brain ultrasound
|
3188aae333ba9260a81ab010b5c53d254b44a9ab65b298c8bdd4d1b87bf2f0b5 | Text | 83 | 2 | # Wrightetal_2023
Codes used for fiber photometry analyses and behavioral tracking
|
4c9e04ec106824ce876a98e470a9ff83f23fb9171bddf057f2bf3e4f44b7b4ec | Text | 87 | 2 | # rodent_dbs
Analysis scripts for extracting gait metrics from rodents for publication
|
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