memisc - Management of Survey Data and Presentation of Analysis Results

An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.

Last updated 29 days ago

survey-data

12.50 score 44 stars 13 packages 1.1k scripts 11k downloads

mclogit - Multinomial Logit Models, with or without Random Effects or Overdispersion

Provides estimators for multinomial logit models in their conditional logit and baseline logit variants, with or without random effects, with or without overdispersion. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small.

Last updated 2 months ago

10.86 score 23 stars 3 packages 247 scripts 3.9k downloads

RKernel - Yet another R kernel for Jupyter

Provides a kernel for Jupyter.

Last updated 16 days ago

jupyterjupyter-kerneljupyter-kernelsjupyter-notebook

4.31 score 37 stars

munfold - Metric Unfolding

Multidimensional unfolding using Schoenemann's algorithm for metric and Procrustes rotation of unfolding results.

Last updated 11 months ago

2.70 score 1 stars 2 scripts 206 downloads

iimm - Improved Infrence for Multilevel Models with Few Clusters

Support for inference about linear mixed effects models estimated with 'lmer' from package 'lme4' using a Student's t-distribution with degrees of freedom determined by the m-l-1 heuristic or the Kenward-Roger method.

Last updated 6 years ago

2.40 score 5 stars

mpred - Generic Predictive Margins

Provides a function to compute predictive margins.

Last updated 3 years ago

1.00 score

EMfit - An Infrastructure for Latent Variable Model Fitting using EM Algorithms

The package contains the boilerplate code for estimating latent variable models by maximum marginal likelihood using EM algorithms. The code is generic and thus not optimised for particular models.

Last updated 5 years ago

1.00 score