qPCR4vir

Ariel Vina-Rodriguez's starred repositories

Probit-analysis

In toxicology, probit analysis is an important tool to interpret bioassay results. In this folder, you can find the guidelines, R script, Excel worksheet and example data set to run the probit analysis with your own data.

Language:R300

bayes_probit_graphical_causal

Implements the Bayesian methodology of Castelletti & Consonni (2021, Bayesian Analysis) for structure learning and causal inference in probit graphical models.

Language:R200

BayesianProbit_MH_Gibbs_Inference

Explore Bayesian inference within a probit model for binary outcome data using MCMC techniques. This repo implements Metropolis-Hastings and Gibbs sampling to infer model parameters, offering insights into their performance through autocorrelation analysis and posterior visualization.

Language:Python100

logistic-regression

In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc... Each object being detected in the image would be assigned a probability between 0 and 1 and the sum adding to one. Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled "0" and "1". In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names. Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary dependent variable this generalizes the odds ratio.

Language:Jupyter Notebook100

probit

probit is a simple and accessible Gaussian process package in JAX

Language:PythonMIT800

DuckX

C++ library for creating and modifying Microsoft Word (.docx) files

Language:C++MIT37900

libxlsxwriter

A C library for creating Excel XLSX files.

Language:CNOASSERTION141800

nana

A modern C++ GUI library

BSL-1.0100

bin2cpp

bin2cpp: The easiest way to embed small files into a c++ executable. bin2cpp converts text or binary files to C++ files (*.h, *.cpp) for easy access within the code.

Language:C++MIT6900

monty_hall_problem

This is a simulation of Monty Hall problem in C++ with nana library for GUI. The program demonstrates how to apply MVC architecture to application development.

Language:C++MIT100