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The Python Statsmodels package supports binomial and poisson implementations. [14] The Julia package MixedModels.jl provides a function called GeneralizedLinearMixedModel that fits a generalized linear mixed model to provided data. [15] DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models (utk.edu) [16]
StatXact – package for exact nonparametric and parametric statistics; SuperCROSS – comprehensive statistics package with ad-hoc, cross tabulation analysis; Systat – general statistics package; The Unscrambler – free-to-try commercial multivariate analysis software for Windows; Unistat – general statistics package that can also work as ...
Product One-way Two-way MANOVA GLM Mixed model Post-hoc Latin squares; ADaMSoft: Yes Yes No No No No No Alteryx: Yes Yes Yes Yes Yes Analyse-it: Yes Yes No
Python: the "statsmodels" package includes models for time series analysis – univariate time series analysis: AR, ARIMA – vector autoregressive models, VAR and structural VAR – descriptive statistics and process models for time series analysis.
In Python, the statsmodels [15] module includes functions for the covariance matrix using Newey–West. In Gretl, the option --robust to several estimation commands (such as ols) in the context of a time-series dataset produces Newey–West standard errors. [16]
gam, an R package for GAMs by backfitting. gam, Python module in statsmodels.gam module. InterpretML, a Python package for fitting GAMs via bagging and boosting. mgcv, an R package for GAMs using penalized regression splines. mboost, an R package for boosting including additive models. gss, an R package for smoothing spline ANOVA.
Currently, this is the method implemented in statistical software such as Python (statsmodels package) and SAS (proc mixed), and as initial step only in R's nlme package lme(). The solution to the mixed model equations is a maximum likelihood estimate when the distribution of the errors is normal. [23] [24]
PyMC (formerly known as PyMC3) is a probabilistic programming language written in Python. It can be used for Bayesian statistical modeling and probabilistic machine learning. It can be used for Bayesian statistical modeling and probabilistic machine learning.