Jos茅 Bayo谩n Santiago Calder贸n (@Nosferican)
github

Nosferican

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  • Stars
    star
    151
  • Global Rank 153,004 (Top 6 %)
  • Followers 80
  • Following 98
  • Registered about 12 years ago
  • Most used languages
    Julia
         65.0 %
    TeX
         15.0 %
    HTML
         15.0 %
    JavaScript
         5.0 %
  • Location 馃嚭馃嚫 United States
  • Country Total Rank 34,256
  • Country Ranking
    Julia
         158
    TeX
         293

Top repositories

1

Econometrics.jl

Econometrics in Julia
TeX
67
star
2

WebDriver.jl

A WebDriver / JSON Wire Protocol Binding Compatible with Selenium for the Julia Language.
Julia
39
star
3

UEM.jl

Unobserved Effects Models (Panel Data Econometrics) -> For Beta Test
Julia
7
star
4

NCEI.jl

Interface for the National Centers for Environmental Information (NCEI) API
Julia
7
star
5

Julia-Ecosystem-Analysis

An analysis of the Julia ecosystem as it relates to OSS packages.
Julia
4
star
6

DependenciesParser.jl

Dependency parser for Julia
Julia
4
star
7

SpringerQuarantineBooks.jl

A tool for downloading the Springer Quarantine Books.
Julia
3
star
8

EconJobMarket.jl

Interface for the EconJobMarket Data API using the Julia programming language
Julia
2
star
9

CandidatosEleccionesGeneralesPR2020

Candidaturas para la elecci贸n general de PR 2020
HTML
2
star
10

EconUtils.jl

For Dev
Julia
1
star
11

LuxAI.jl

An agent for the Kaggle Lux AI Challenge
Julia
1
star
12

WorldBankDataDive2019ConflictSpendingAsia

DataDive 2019 Conflict and Spending in Asia
Julia
1
star
13

LaTeX_Style_cgu

A neat LaTeX class and template for producing manuscripts, dissertations, and others.
TeX
1
star
14

Learning-the-GitHub-API

Tutorial on how to learn to consume the GitHub API
1
star
15

AEEIncidentes

Julia
1
star
16

UVA-SDS-Sample-Lecture

Materials relevant to the "On Campus" interview for the Teaching Track position
Julia
1
star
17

DonativosPol-ticosPR2020

Informaci贸n de la Oficina del Contralor Electoral de PR
Julia
1
star
18

ActionSelenium

JavaScript
1
star
19

On-Cluster-Robust-Models-Old

Empirical work commonly deals with heterogeneous in treatment populations with potential clustered sampling design. Current approaches to overcome issues that arise in these cases include weighted least squares, fixed effects models, and cluster robust variance covariance estimators. However, the current solutions often offer sub-optimal performance or practitioners misuse them. This study examines possible disadvantages to using non-cluster robust models and alternative solutions. Some of the contributions include a framework to understand and develop cluster robust models with new advise for practitioners and practical considerations. (JEL C01)
TeX
1
star
20

JBSC

HTML
1
star
21

PRLegVotos

Informaci贸n sobre las votaciones de medidas legislativas de Puerto Rico.
HTML
1
star