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I presented my master’s project in October 2023. It was titled ‘Reliability Estimation in Series Systems: Maximum Likelihood Techniques for Right-Censored and Masked Failure Data’.
A minimalist automatic differentiation library in R, inspired by micrograd, for educational purposes and simple neural network implementations
Most R packages hardcode specific likelihood models. likelihood.model provides a generic framework where likelihoods are first-class composable objects—designed to work seamlessly with algebraic.mle for maximum likelihood estimation.
R’s hypothesis testing functions are inconsistent—t.test() returns different structures than chisq.test(), making generic workflows painful. hypothesize provides a unified API so any test returns the same interface: p-value, test statistic, …
An R package providing an algebraic framework for maximum likelihood estimators with well-defined statistical properties
This problem set covers the E-M algorithm for right-censored normal data with known variance.
This problem set covers sampling from a Gamma distribution using Metropolis-Hastings and acceptance-rejection methods.
Most survival analysis forces you to pick from a catalog—Weibull, exponential, log-normal. dfr.dist flips this: you specify the hazard function directly, and it handles all the math.
Instead of choosing Weibull(shape, scale), you …
Maximum likelihood estimators have rich mathematical structure—they’re consistent, asymptotically normal, efficient. algebraic.mle exposes this structure through an algebra where MLEs are objects you compose, transform, and query.
Most statistical software treats probability distributions as static parameter sets you pass to sampling or density functions. algebraic.dist takes a different approach: distributions are algebraic objects that compose, transform, and combine using …
One of the best parts of my mathematics degree is deepening my R skills—not just using R packages, but building them.
R has a unique position in statistics: