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Linear Models for Panel Data
A set of estimators for models and (robust) covariance matrices, and tests for panel data
econometrics, including within/fixed effects, random effects, between, first-difference,
nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models,
panel generalized method of moments (GMM) and general FGLS models,
mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators
with common factors, variable coefficients and limited dependent variables models.
Test functions include model specification, serial correlation, cross-sectional dependence,
panel unit root and panel Granger (non-)causality. Typical references are general econometrics
text books such as Baltagi (2021), Econometric Analysis of Panel Data (
Lightning Fast Serialization of Data Frames
Multithreaded serialization of compressed data frames using the 'fst' format. The 'fst' format allows for full random access of stored data and a wide range of compression settings using the LZ4 and ZSTD compressors.
Multivariate Dependence with Copulas
Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.
Mixed Effects Cox Models
Fit Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002.
Generalized Random Forests
Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.
Survey Sampling
Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators.
Multinomial Logit Models
Maximum likelihood estimation of random utility discrete
choice models. The software is described in Croissant (2020)
Define and Work with Parameter Spaces for Complex Algorithms
Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.
Truncated Multivariate Normal and Student t Distribution
Random number generation for the truncated multivariate normal and Student t distribution. Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
General Package for Meta-Analysis
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker