Title: 'Rcpp' Bindings for 'Annoy', a Library for Approximate Nearest
Neighbors
Description: 'Annoy' is a small C++ library for Approximate Nearest Neighbors
written for efficient memory usage as well an ability to load from / save to
disk. This package provides an R interface by relying on the 'Rcpp' package,
exposing the same interface as the original Python wrapper to 'Annoy'. See
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel
Diff between RcppAnnoy versions 0.0.5 dated 2015-01-24 and 0.0.6 dated 2015-05-03
ChangeLog | 26 +++++++++++- DESCRIPTION | 24 +++++------ MD5 | 12 ++--- README.md | 13 +++--- cleanup | 2 inst/include/annoylib.h | 104 +++++++++++++++++++++++++++++++----------------- src/annoy.cpp | 8 ++- 7 files changed, 124 insertions(+), 65 deletions(-)
Title: Distributed Model-Based Boosting
Description: Distributed gradient boosting based on the mboost package. The
parboost package is designed to scale up component-wise functional
gradient boosting in a distributed memory environment by splitting the
observations into disjoint subsets, or alternatively using bootstrap
samples (bagging). Each cluster node then fits a boosting model to its
subset of the data. These boosting models are combined in an ensemble,
either with equal weights, or by fitting a (penalized) regression
model on the predictions of the individual models on the complete
data.
Author: Ronert Obst
Maintainer: Ronert Obst
Diff between parboost versions 0.1.3 dated 2014-03-02 and 0.1.4 dated 2015-05-03
DESCRIPTION | 10 - MD5 | 31 ++-- NAMESPACE | 7 + NEWS | 5 R/methods.R | 34 ++++- R/parboost-package.R | 3 inst/CITATION | 26 ++- man/coef.parboost.Rd | 45 +++--- man/cv_subsample.Rd | 29 ++-- man/friedman2.Rd | 27 ++-- man/parboost.Rd | 280 ++++++++++++++++++++---------------------- man/parboost_fit.Rd | 47 +++---- man/postprocess.Rd | 17 +- man/predict.parboost.Rd | 32 ++-- man/print.parboost.Rd |only man/print.summary.parboost.Rd |only man/selected.parboost.Rd | 13 + man/summary.parboost.Rd |only 18 files changed, 319 insertions(+), 287 deletions(-)
Title: Multivariate Comparative Tools for Fitting Evolutionary Models
to Morphometric Data
Description: Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees.
Author: Julien Clavel, with contributions from Aaron King, and Emmanuel Paradis
Maintainer: Julien Clavel
Diff between mvMORPH versions 1.0.4 dated 2015-03-22 and 1.0.5 dated 2015-05-03
DESCRIPTION | 8 - MD5 | 41 +++++---- NAMESPACE | 7 + R/fun.r | 70 ++++++++++++++++ R/mvBM.r | 191 ++++++++++++++++++++++++++++++++++++++-------- R/mvEB.r | 41 ++++----- R/mvLLIK.r | 87 ++++++++++++-------- R/mvOU.r | 16 ++- R/mvSHIFT.r | 4 R/mvSIM.r | 9 +- R/testLRT.r | 20 ++++ man/LRT.Rd | 5 - man/halflife.Rd |only man/mvBM.Rd | 4 man/mvEB.Rd | 29 ++---- man/mvLL.Rd | 4 man/mvMORPH-package.Rd | 6 - man/mvOU.Rd | 2 man/stationary.Rd |only src/covar-matrix-simmap.c | 1 src/mvmorph-covar-mat.c | 2 src/pic_loglik_mvmorph.c | 37 ++++++-- src/spherical.c |only 23 files changed, 418 insertions(+), 166 deletions(-)
Title: Model and Analyse Interval Data
Description: Implements methodologies for modelling interval data, considering appropriate parameterizations of the variance-covariance matrix that take into account the intrinsic nature of interval data, and lead to five different possible configuration structures.
Author: Pedro Duarte Silva
Maintainer: Pedro Duarte Silva
Diff between MAINT.Data versions 0.3 dated 2014-10-24 and 0.4 dated 2015-05-03
CHANGELOG | 5 +++++ DESCRIPTION | 13 +++++++------ MD5 | 9 +++++---- NAMESPACE | 3 --- R/ClasGenMetDef.R | 12 +++++++++--- man/stdEr.Rd |only 6 files changed, 26 insertions(+), 16 deletions(-)
Title: Create and Manipulate Discrete Random Variables
Description: Create, manipulate, transform, and simulate from discrete random
variables. The syntax is modeled after that which is used in mathematical
statistics and probability courses, but with powerful support for more
advanced probability calculations. This includes the creation of joint
random variables, and the derivation and manipulation of their conditional
and marginal distributions.
Author: Andreas Buja [aut],
Eric Hare [aut, cre],
Heike Hofmann [aut]
Maintainer: Eric Hare
Diff between discreteRV versions 1.2 dated 2015-04-10 and 1.2.1 dated 2015-05-03
DESCRIPTION | 19 +++++++------ MD5 | 67 +++++++++++++++++++++++++---------------------- NAMESPACE | 2 - build |only inst |only man/E.Rd | 2 - man/KURT.Rd | 2 - man/P.Rd | 2 - man/Prop.Rd | 2 - man/RV.Rd | 2 - man/SD.Rd | 2 - man/SKEW.Rd | 2 - man/SofI.Rd | 2 - man/SofIID.Rd | 2 - man/V.Rd | 2 - man/as.RV.Rd | 2 - man/grapes-AND-grapes.Rd | 2 - man/grapes-OR-grapes.Rd | 2 - man/grapes-in-grapes.Rd | 2 - man/iid.Rd | 2 - man/independent.Rd | 2 - man/joint.Rd | 2 - man/jointRV.Rd | 2 - man/marginal.Rd | 2 - man/margins.Rd | 2 - man/outcomes.Rd | 2 - man/plot.RV.Rd | 2 - man/plot.RVsim.Rd | 2 - man/print.RV.Rd | 2 - man/probs.Rd | 2 - man/props.Rd | 2 - man/qqnorm.RV.Rd | 2 - man/rsim.Rd | 2 - man/skewSim.Rd | 2 - vignettes |only 35 files changed, 76 insertions(+), 70 deletions(-)
Title: Large, Sparse Optimal Matching with Refined Covariate Balance
Description: Tools for large, sparse optimal matching of treated units
and control units in observational studies. Provisions are
made for refined covariate balance constraints, which include
fine and near-fine balance as special cases. Matches are
optimal in the sense that they are computed as solutions to
network optimization problems rather than greedy algorithms.
Author: Samuel D. Pimentel
Maintainer: Samuel D. Pimentel
Diff between rcbalance versions 1.4 dated 2015-03-24 and 1.5 dated 2015-05-03
DESCRIPTION | 8 +-- MD5 | 12 ++--- R/rcbalance-internal.R | 27 ++++++++++++- R/rcbalance.R | 96 ++++++++++++++++++++++++++--------------------- man/dist2net.Rd | 6 ++ man/rcbalance-package.Rd | 4 - man/rcbalance.Rd | 8 ++- 7 files changed, 103 insertions(+), 58 deletions(-)
More information about QCAfalsePositive at CRAN
Permanent link
Title: Nested Association Mapping Analysis
Description: Designed for association studies in nested association mapping (NAM) panels, also handling biparental and random panels. It includes functions for genome-wide associations mapping of multiple populations, marker quality control, solving mixed models and finding variance components through REML and Gibbs sampling.
Author: Alencar Xavier, William Muir, Katy Rainey, Tiago Pimenta, Qishan Wang, Shizhong Xu.
Maintainer: Alencar Xavier
Diff between NAM versions 1.3 dated 2015-04-22 and 1.3.2 dated 2015-05-03
DESCRIPTION | 10 +- MD5 | 38 ++++----- NAMESPACE | 2 R/Fst.R | 5 - R/MCreml.R | 15 --- R/RcppExports.R | 62 +++++++-------- R/gibbs.R | 66 ++++++++++++---- R/gwas.R | 8 + R/gwas2.R | 10 ++ R/manhattan.R | 27 ++++-- R/reference.R | 1 R/reml.R | 213 ++++++++++++++++++++++++++++++----------------------- R/snpH2.R | 5 - R/snpQC.R | 86 ++++++++++++++++++++- man/Internals.Rd | 2 man/NAM-package.Rd | 4 man/gibbs.Rd | 6 + man/reml.Rd | 4 man/snpH2.Rd | 2 man/snpQC.Rd | 83 ++++++++++---------- 20 files changed, 408 insertions(+), 241 deletions(-)
Title: Mapping Smoothed Effect Estimates from Individual-Level Data
Description: Contains functions for mapping odds ratios or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting for confounding variables, using methods described by Kelsall and Diggle (1998) and Webster at al. (2006). Includes convenient functions for mapping, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).
Author: Veronica Vieira, Scott Bartell, and Robin Bliss
Maintainer: Scott Bartell
Diff between MapGAM versions 0.7-4 dated 2014-12-10 and 0.7-5 dated 2015-05-03
ChangeLog | 10 ++++++++++ DESCRIPTION | 8 ++++---- MD5 | 16 ++++++++-------- R/colormap.R | 19 +++++++++++++++---- R/trimdata.R | 2 +- man/MAdata.Rd | 2 +- man/MapGAM-package.Rd | 6 +++--- man/colormap.Rd | 5 ++++- man/modgam.Rd | 5 +---- 9 files changed, 47 insertions(+), 26 deletions(-)
Title: Bayesian Network Structure Learning, Parameter Learning and
Inference
Description: Bayesian network structure learning, parameter learning and
inference.
This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB,
MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms
for discrete, Gaussian and conditional Gaussian networks, along with many
score functions and conditional independence tests.
The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are
also implemented.
Some utility functions (model comparison and manipulation, random data
generation, arc orientation testing, simple and advanced plots) are
included, as well as support for parameter estimation (maximum likelihood
and Bayesian) and inference, conditional probability queries and
cross-validation. Development snapshots with the latest bugfixes are
available from www.bnlearn.com.
Author: Marco Scutari
Maintainer: Marco Scutari
Diff between bnlearn versions 3.7.1 dated 2015-01-23 and 3.8 dated 2015-05-03
bnlearn-3.7.1/bnlearn/src/common.h |only bnlearn-3.8/bnlearn/Changelog | 29 + bnlearn-3.8/bnlearn/DESCRIPTION | 11 bnlearn-3.8/bnlearn/MD5 | 216 ++++++------- bnlearn-3.8/bnlearn/NAMESPACE | 13 bnlearn-3.8/bnlearn/R/arc.operations.R | 2 bnlearn-3.8/bnlearn/R/custom.fit.R | 23 - bnlearn-3.8/bnlearn/R/fit.R | 15 bnlearn-3.8/bnlearn/R/fitted.assignment.R | 29 - bnlearn-3.8/bnlearn/R/foreign-read.R | 22 - bnlearn-3.8/bnlearn/R/frontend-data.R | 11 bnlearn-3.8/bnlearn/R/frontend-fit.R | 81 +++- bnlearn-3.8/bnlearn/R/frontend-graph.R | 2 bnlearn-3.8/bnlearn/R/frontend-packages.R | 19 - bnlearn-3.8/bnlearn/R/frontend-predict.R | 74 ---- bnlearn-3.8/bnlearn/R/lattice.R | 2 bnlearn-3.8/bnlearn/R/learning-algorithms.R | 25 - bnlearn-3.8/bnlearn/R/utils-cluster.R | 6 bnlearn-3.8/bnlearn/R/utils-graph.R | 3 bnlearn-3.8/bnlearn/R/utils-misc.R | 42 ++ bnlearn-3.8/bnlearn/R/utils-sanitization.R | 128 +++++-- bnlearn-3.8/bnlearn/inst/CITATION | 4 bnlearn-3.8/bnlearn/inst/network.scripts/clgaussian.test.R | 2 bnlearn-3.8/bnlearn/man/bn.fit.methods.Rd | 64 ++- bnlearn-3.8/bnlearn/man/bnlearn-package.Rd | 4 bnlearn-3.8/bnlearn/man/compare.Rd | 7 bnlearn-3.8/bnlearn/man/configs.Rd |only bnlearn-3.8/bnlearn/man/cpquery.Rd | 2 bnlearn-3.8/bnlearn/src/acyclic.c | 7 bnlearn-3.8/bnlearn/src/all.monte.carlo.c | 3 bnlearn-3.8/bnlearn/src/allocations.c | 2 bnlearn-3.8/bnlearn/src/allsubs.test.c | 12 bnlearn-3.8/bnlearn/src/arcs2amat.c | 4 bnlearn-3.8/bnlearn/src/arcs2elist.c | 3 bnlearn-3.8/bnlearn/src/averaging.c | 5 bnlearn-3.8/bnlearn/src/bayesian.network.c | 3 bnlearn-3.8/bnlearn/src/bind.c | 4 bnlearn-3.8/bnlearn/src/bn.recovery.c | 4 bnlearn-3.8/bnlearn/src/bootstrap.c | 6 bnlearn-3.8/bnlearn/src/cache.structure.c | 4 bnlearn-3.8/bnlearn/src/cg.assumptions.c | 6 bnlearn-3.8/bnlearn/src/cg.loglikelihood.c | 9 bnlearn-3.8/bnlearn/src/cg.mutual.information.c | 5 bnlearn-3.8/bnlearn/src/common.c | 5 bnlearn-3.8/bnlearn/src/configurations.c | 4 bnlearn-3.8/bnlearn/src/covariance.c | 3 bnlearn-3.8/bnlearn/src/cpdag.c | 5 bnlearn-3.8/bnlearn/src/cpdist.c | 6 bnlearn-3.8/bnlearn/src/ctest.c | 12 bnlearn-3.8/bnlearn/src/data.frame.c | 3 bnlearn-3.8/bnlearn/src/dedup.c | 5 bnlearn-3.8/bnlearn/src/df.adjust.c | 2 bnlearn-3.8/bnlearn/src/dirichlet.posterior.c | 6 bnlearn-3.8/bnlearn/src/discrete.loglikelihood.c | 5 bnlearn-3.8/bnlearn/src/discrete.monte.carlo.c | 7 bnlearn-3.8/bnlearn/src/discrete.mutual.information.c | 4 bnlearn-3.8/bnlearn/src/filter.arcs.c | 5 bnlearn-3.8/bnlearn/src/fitted.c | 4 bnlearn-3.8/bnlearn/src/gaussian.loglikelihood.c | 7 bnlearn-3.8/bnlearn/src/gaussian.monte.carlo.c | 10 bnlearn-3.8/bnlearn/src/gaussian.mutual.information.c | 3 bnlearn-3.8/bnlearn/src/globals.c | 2 bnlearn-3.8/bnlearn/src/graph.generation.c | 8 bnlearn-3.8/bnlearn/src/graph.priors.c | 7 bnlearn-3.8/bnlearn/src/hash.c | 3 bnlearn-3.8/bnlearn/src/hc.cache.lookup.c | 8 bnlearn-3.8/bnlearn/src/htest.c | 5 bnlearn-3.8/bnlearn/src/include |only bnlearn-3.8/bnlearn/src/indep.test.c | 3 bnlearn-3.8/bnlearn/src/is.dag.c | 4 bnlearn-3.8/bnlearn/src/is.row.equal.c | 2 bnlearn-3.8/bnlearn/src/jonckheere.c | 5 bnlearn-3.8/bnlearn/src/likelihood.weighting.c | 3 bnlearn-3.8/bnlearn/src/linear.algebra.c | 7 bnlearn-3.8/bnlearn/src/linear.correlation.c | 5 bnlearn-3.8/bnlearn/src/loss.c | 7 bnlearn-3.8/bnlearn/src/map.lw.c | 5 bnlearn-3.8/bnlearn/src/mi.matrix.c | 7 bnlearn-3.8/bnlearn/src/mvber.c | 6 bnlearn-3.8/bnlearn/src/nparams.c | 3 bnlearn-3.8/bnlearn/src/parse.c | 2 bnlearn-3.8/bnlearn/src/path.c | 5 bnlearn-3.8/bnlearn/src/pdag2dag.c | 10 bnlearn-3.8/bnlearn/src/pearson.x2.c | 4 bnlearn-3.8/bnlearn/src/per.node.score.c | 6 bnlearn-3.8/bnlearn/src/predict.c | 7 bnlearn-3.8/bnlearn/src/rbn.c | 8 bnlearn-3.8/bnlearn/src/rcont2.c | 3 bnlearn-3.8/bnlearn/src/sampling.c | 3 bnlearn-3.8/bnlearn/src/sanitization.c | 2 bnlearn-3.8/bnlearn/src/score.delta.c | 4 bnlearn-3.8/bnlearn/src/shd.c | 4 bnlearn-3.8/bnlearn/src/shrinkage.c | 7 bnlearn-3.8/bnlearn/src/simulation.c | 2 bnlearn-3.8/bnlearn/src/strings.c | 2 bnlearn-3.8/bnlearn/src/subsets.c | 5 bnlearn-3.8/bnlearn/src/symmetric.c | 3 bnlearn-3.8/bnlearn/src/tabu.c | 6 bnlearn-3.8/bnlearn/src/test.counter.c | 2 bnlearn-3.8/bnlearn/src/tiers.c | 4 bnlearn-3.8/bnlearn/src/utest.c | 7 bnlearn-3.8/bnlearn/src/which.max.c | 3 bnlearn-3.8/bnlearn/src/wishart.posterior.c | 8 103 files changed, 751 insertions(+), 455 deletions(-)