CRAN
gemtc 0.8.1
Network Meta-Analysis Using Bayesian Methods
Released Sep 6, 2016 by Gert van Valkenhoef
Dependencies
truncnorm 1.0-7 igraph 1.1.2 coda 0.19-1 plyr 1.8.4 Rglpk 0.6-3 rjags 4-6 meta 4.8-4
Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations.
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml
file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies> <dependency> <groupId>org.renjin.cran</groupId> <artifactId>gemtc</artifactId> <version>0.8.1-b17</version> </dependency> </dependencies> <repositories> <repository> <id>bedatadriven</id> <name>bedatadriven public repo</name> <url>https://nexus.bedatadriven.com/content/groups/public/</url> </repository> </repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:gemtc')
Test Results
This package was last tested against Renjin 0.8.2504 on Oct 30, 2017.
- Multi-arm_trial_decomposition.Fixed_effects_MA_recovers_variances_E1
- Multi-arm_trial_decomposition.Fixed_effects_MA_recovers_variances_E2
- Multi-arm_trial_decomposition.Fixed_effects_MA_recovers_variances_E3
- Multi-arm_trial_decomposition.Fixed_effects_MA_recovers_variances_E4
- blobbogram-examples
- inits_to_monitors.inits_to_monitors_adds_sd_d_if_tau_d_is_present
- inits_to_monitors.inits_to_monitors_adds_sd_d_if_var_d_is_present
- inits_to_monitors.inits_to_monitors_maps_matrices
- inits_to_monitors.inits_to_monitors_maps_scalars
- inits_to_monitors.inits_to_monitors_maps_vectors
- inits_to_monitors.inits_to_monitors_removes_NAs_from_matrices
- inits_to_monitors.inits_to_monitors_removes_NAs_from_vectors
- likelihood/link.binom_cloglog_is_defined
- likelihood/link.binom_log_is_defined
- likelihood/link.binom_logit_is_defined
- likelihood/link.normal_identity_is_defined
- likelihood/link.poisson_log_is_defined
- ll.call-examples
- ll_binom_log.mtc_arm_mle_(default_correction)_E1
- ll_binom_log.mtc_arm_mle_(default_correction)_E2
- ll_binom_log.mtc_arm_mle_(default_correction)_E3
- ll_binom_log.mtc_arm_mle_(no_correction)
- ll_binom_log.mtc_arm_mle_(other_correction)
- ll_binom_log.mtc_rel_mle_(alternative_magnitude_correction)
- ll_binom_log.mtc_rel_mle_(as-needed_default_correction)_E1
- ll_binom_log.mtc_rel_mle_(as-needed_default_correction)_E2
- ll_binom_log.mtc_rel_mle_(as-needed_default_correction)_E3
- ll_binom_log.mtc_rel_mle_(forced_default_correction)_E1
- ll_binom_log.mtc_rel_mle_(forced_default_correction)_E2
- ll_binom_log.mtc_rel_mle_(reciprocal_correction)_E1
- ll_binom_log.mtc_rel_mle_(reciprocal_correction)_E2
- ll_binom_log.mtc_rel_mle_(reciprocal_correction)_E3
- ll_binom_log.mtc_rel_mle_(reciprocal_correction)_E4
- ll_binom_logit.mtc_arm_mle_(default_correction)_E1
- ll_binom_logit.mtc_arm_mle_(default_correction)_E2
- ll_binom_logit.mtc_arm_mle_(default_correction)_E3
- ll_binom_logit.mtc_arm_mle_(no_correction)
- ll_binom_logit.mtc_arm_mle_(other_correction)_E1
- ll_binom_logit.mtc_arm_mle_(other_correction)_E2
- ll_binom_logit.mtc_arm_mle_(other_correction)_E3
- ll_binom_logit.mtc_rel_mle_(alternative_magnitude_correction)
- ll_binom_logit.mtc_rel_mle_(as-needed_default_correction)_E1
- ll_binom_logit.mtc_rel_mle_(as-needed_default_correction)_E2
- ll_binom_logit.mtc_rel_mle_(as-needed_default_correction)_E3
- ll_binom_logit.mtc_rel_mle_(forced_default_correction)_E1
- ll_binom_logit.mtc_rel_mle_(forced_default_correction)_E2
- ll_binom_logit.mtc_rel_mle_(reciprocal_correction)_E1
- ll_binom_logit.mtc_rel_mle_(reciprocal_correction)_E2
- ll_binom_logit.mtc_rel_mle_(reciprocal_correction)_E3
- ll_binom_logit.mtc_rel_mle_(reciprocal_correction)_E4
- ll_poisson_log.mtc_arm_mle_(default_correction)_E1
- ll_poisson_log.mtc_arm_mle_(default_correction)_E2
- ll_poisson_log.mtc_arm_mle_(default_correction)_E3
- ll_poisson_log.mtc_arm_mle_(no_correction)
- ll_poisson_log.mtc_arm_mle_(other_correction)
- ll_poisson_log.mtc_rel_mle_(alternative_magnitude_correction)
- ll_poisson_log.mtc_rel_mle_(as-needed_default_correction)_E1
- ll_poisson_log.mtc_rel_mle_(as-needed_default_correction)_E2
- ll_poisson_log.mtc_rel_mle_(as-needed_default_correction)_E3
- ll_poisson_log.mtc_rel_mle_(forced_default_correction)_E1
- ll_poisson_log.mtc_rel_mle_(forced_default_correction)_E2
- ll_poisson_log.mtc_rel_mle_(reciprocal_correction)_E1
- ll_poisson_log.mtc_rel_mle_(reciprocal_correction)_E2
- ll_poisson_log.mtc_rel_mle_(reciprocal_correction)_E3
- ll_poisson_log.mtc_rel_mle_(reciprocal_correction)_E4
- ll_poisson_log.mtc_rel_mle_(reciprocal_correction)_E5
- mtc.data.studyrow-examples
- mtc.hy.prior-examples
- mtc.network-examples
- mtc.nodesplit-examples
- mtc.run-examples
- mtc_hy_prior.LOR_empirical_priors_have_correct_values_E1
- mtc_hy_prior.LOR_empirical_priors_have_correct_values_E2
- mtc_hy_prior.LOR_empirical_priors_have_correct_values_E3
- mtc_hy_prior.other_priors_can_be_specified_by_name_E1
- mtc_hy_prior.other_priors_can_be_specified_by_name_E2
- mtc_hy_prior.priors_can_have_more_or_less_than_two_parameters_E1
- mtc_hy_prior.priors_can_have_more_or_less_than_two_parameters_E2
- mtc_hy_prior.the_prior_can_be_specified_on_the_precision
- mtc_hy_prior.the_prior_can_be_specified_on_the_variance
- mtc_hy_prior.the_standard_uniform_prior_on_standard_deviation_is_generated_correctly_E1
- mtc_hy_prior.the_standard_uniform_prior_on_standard_deviation_is_generated_correctly_E2
- mtc_hy_prior.the_standard_uniform_prior_on_standard_deviation_is_generated_correctly_E3
- mtc_model_(column_handling).normal_identity_requires_the_right_columns
- mtc_model_ume.Vertices_agree_between_mtc_network_graph_and_ume_model$graph
- rank.probability-examples
- read.mtc.network-examples
- rel_mle_[ab|re].a_single_pair_returns_a_one-row_matrix
- rel_mle_[ab|re].two_pairs_return_a_two-row_matrix
- relative.effect-examples
- relative.effect.table-examples
- test