R interface to Fortran 77 implementation of CALINE3

The code takes an n x p data matrix and fits a Causal Additive Model (CAM) for estimating the causal structure of the underlying process. The output is a p x p adjacency matrix (a one in entry (i,j) indicates an edge from i to j). Details of the algorithm can be found in: P. Bühlmann, J. Peters, J. Ernest: "CAM: Causal Additive Models, high-dimensional order search and penalized regression", Annals of Statistics 42:2526-2556, 2014.

Finite Mixture Models and meta-analysis tools - based on C.A.MAN
Peter Schlattmann <peter.schlattmann@mti.uni-jena.de>, Johannes
Hoehne
Tools for the analysis of finite semiparametric mixtures.
These are useful when data is heterogeneous, e.g. in
pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
(flexible support size) and EM algorithms (finite support case)
are provided.

Tools for the analysis of finite semiparametric mixtures.
These are useful when data is heterogeneous, e.g. in
pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
(flexible support size) and EM algorithms (fixed support size)
are provided for univariate and bivariate data

Peter Schlattmann <peter.schlattmann@mti.uni-jena.de>, Johannes
Hoehne, Maryna Verba

Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN
Tools for the analysis of finite semiparametric mixtures.
These are useful when data is heterogeneous, e.g. in
pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
(flexible support size) and EM algorithms (fixed support size)
are provided for univariate and bivariate data.

Tools for the analysis of finite semiparametric mixtures.
These are useful when data is heterogeneous, e.g. in
pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
(flexible support size) and EM algorithms (fixed support size)
are provided for univariate (Bohning et al., 1992;
<doi:10.2307/2532756>) and bivariate data (Schlattmann et
al., 2015; <doi:10.1016/j.jclinepi.2014.08.013>).

Cell-Typing using Variance Adjusted Mahalanobis Distances with
Multi-Labeling
c(
person("H. Robert", "Frost", email = "hildreth.r.frost@dartmouth.edu", role = c("aut")),
person("Courtney", "Schiebout", email = "courtney.t.schiebout.gr@dartmouth.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4830-8237")))
Creates multi-label cell-types for single-cell RNA-sequencing data based on weighted VAM scoring of cell-type specific gene sets. Schiebout, Frost (2022) <https://psb.stanford.edu/psb-online/proceedings/psb22/schiebout.pdf>.
VAM, Seurat (>= 4.0.0), MASS, Matrix, dplyr, utils, edgeR,
org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, SeuratObject (>=
4.0.0)
H. Robert Frost [aut],
Courtney Schiebout [aut, cre] (<https://orcid.org/0000-0002-4830-8237>)