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.

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survey-data

12.25 score 48 stars 15 dependents 1.3k scripts 13k downloads

mclogit - Multinomial Logit Models for Categorical Responses and Discrete Choices

Provides estimators for multinomial logit models in their conditional logit (for discrete choices) and baseline logit variants (for categorical responses), optionally with overdispersion or random effects. 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.

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11.93 score 23 stars 4 dependents 303 scripts 12k downloads

RKernel - Yet Another R Kernel for Jupyter

Provides a kernel for Jupyter.

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jupyterjupyter-kerneljupyter-kernelsjupyter-notebook

4.56 score 43 stars 1 scripts

munfold - Metric Unfolding

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

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3.70 score 1 stars 2 scripts 328 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.

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2.40 score 5 stars

mpred - Generic Predictive Margins

Provides a function to compute predictive margins.

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1.70 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. The package supports fitting latent class or finite mixture models using simple EM algorithms and (to some degree) latent trait or continous mixture models using Monte Carlo EM algorithms. Only importance sampling is supported for MCEM. However, it implements automatic sample size adjustment using the approach of Caffo Jank and Jones.

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1.00 score