The vast majority of these code snippets are conceptual demonstrations of more complicated models. The audience is generally faculty, researchers, and graduate students in applied fields who, like I did, want to go beyond their basic statistical training. However, I hope it helps anyone who happens to stumble across it.
standard linear regression, standard logistic regression, penalized regression, lasso regression, ridge regression, newton and IRLS, nelder-mead (Python) (R), gradient descent regression (stochastic), bivariate probit, heckman selection, tobit, naive bayes, multinomial regression, ordinal regression, quantile regression, hurdle poisson, hurdle negbin, zero-inflated poisson, zero-inflated negbin, Cox survival, confirmatory factor analysis, Markov model, hidden Markov model (R) (Python), stochastic volatility, extreme learning machine, Chinese restaurant process, Indian buffet process, One-line models (an exercise), …
one factor random effects (R) (Julia) (Matlab), two factor random effects (R) (Julia) (Matlab), mixed model via ML, mixed model, mixed model with correlated random effects, See the documents section for more…
BEST t-test, linear regression (Compare with BUGS version, JAGS), mixed model, mixed model with correlated random effects, beta regression, mixed model with beta response (Stan) (JAGS), mixture model, topic model, multinomial models, multilevel mediation, variational bayes regression, gaussian process, horseshoe prior, item response theory, …
EM mixture univariate, EM mixture multivariate, EM probit, EM pca, EM probabilistic pca, EM state space model
Gaussian Process noisy, Gaussian Process noise-free, reproducing kernel hilbert space regression, Bayesian Gaussian process, …
cubic spline, …
FizzBuzz test (R) (julia) (Python), Reverse a string recursively (R) (Python), Recursive Word Wrap (R) (Python), calculate compound interest recursively, get US Congress roll call data, Scrape xkcd (R) (Python), Shakespearean Insulter, spurious correlation with ratios, R matrix speedups, …
If you see mistakes or want to suggest changes, please create an issue on the source repository.