Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the 'General Framework for Regression with Mismatched Data' developed by Slawski et al. (2023) <doi:10.48550/arXiv.2306.00909>. The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches).

We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).

Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.

It performs a fast multi-trait genome-wide association analysis based on seemingly unrelated regressions. It tests for pleiotropic effects based on a series of Intersection-Union Wald tests. The package can handle large and unbalanced data and plot results.

Log-multiplicative association models (LMA) are
    models for cross-classifications of categorical variables
    where interactions are represented by products of category
    scale values and an association parameter. Maximum
    likelihood estimation (MLE) fails for moderate to large
    numbers of categorical variables. The 'pleLMA' package
    overcomes this limitation of MLE by using pseudo-likelihood
    estimation to fit the models to small or large
    cross-classifications dichotomous or multi-category variables.
    Originally proposed by Besag (1974,
    <doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood
    estimation takes large complex models and breaks it down
    into smaller ones. Rather than maximizing the likelihood
    of the joint distribution of all the variables, a
    pseudo-likelihood function, which is the product likelihoods
    from conditional distributions, is maximized. LMA models can
    be derived from a number of different frameworks including
    (but not limited to) graphical models and uni-dimensional
    and multi-dimensional item response theory models. More
    details about the models and estimation can be found in
    the vignette.