diff --git a/man/adnuts-package.Rd b/man/adnuts-package.Rd
index 587fbbf..a93bf8a 100644
--- a/man/adnuts-package.Rd
+++ b/man/adnuts-package.Rd
@@ -2,6 +2,7 @@
 % Please edit documentation in R/adnuts-package.R
 \docType{package}
 \name{adnuts-package}
+\alias{adnuts}
 \alias{adnuts-package}
 \title{adnuts: No-U-turn sampling for AD Model Builder (ADMB)}
 \description{
diff --git a/man/adnuts.Rd b/man/adnuts.Rd
deleted file mode 100644
index 89aaf50..0000000
--- a/man/adnuts.Rd
+++ /dev/null
@@ -1,77 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/_adnuts-package.R
-\name{adnuts}
-\alias{adnuts}
-\title{adnuts: No-U-turn sampling for AD Model Builder (ADMB)}
-\description{
-Draw Bayesian posterior samples from an ADMB model using the
-no-U-turn MCMC sampler. Adaptation schemes are used so specifying tuning
-parameters is not necessary, and parallel execution reduces overall run
-time.
-}
-\details{
-The software package Stan pioneered the use of no-U-turn (NUTS) sampling
-for Bayesian models (Hoffman and Gelman 2014, Carpenter et
-al. 2017). This algorithm provides fast, efficient sampling across a
-wide range of models, including hierarchical ones, and thus can be used
-as a generic modeling tool (Monnahan et al. 2017). The functionality
-provided by \pkg{adnuts} is based loosely off Stan and \R package
-\pkg{rstan}
-
-The \pkg{adnuts} \R package provides an \R workflow for NUTS
-  sampling for ADMB models (Fournier et al. 2011), including
-  adaptation of step size and metric (mass matrix), parallel
-  execution, and links to diagnostic and inference tools
-  provided by \pkg{rstan} and \pkg{shinystan}.  The ADMB
-  implementation of NUTS code is bundled into the ADMB source
-  itself (as of version 12.0). Thus, when a user builds an
-  ADMB model the NUTS code is incorporated into the model
-  executable. Thus, \pkg{adnuts} simply provides a convenient
-  set of wrappers to more easily execute, diagnose, and make
-  inference on a model. More details can be found in the
-  package vignette.
-
-Note that previous versions of \pkg{adnuts} included
-  functionality for TMB models, but this has been replaced by
-  \pkg{tmbstan} (Kristensen et al. 2016, Monnahan and
-  Kristensen 2018).
-}
-\references{
-Carpenter, B., Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B.,
-  Betancourt, M., Riddell, A., Guo, J.Q., Li, P., Riddell, A.,
-  2017. Stan: A Probabilistic Programming Language.  J Stat
-  Softw. 76:1-29.
-
-Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A.,
-  Maunder, M.N., Nielsen, A., Sibert, J., 2012. AD Model Builder: using
-  automatic differentiation for statistical inference of highly
-  parameterized complex nonlinear models.  Optim Method
-  Softw. 27:233-249.
-
-Hoffman, M.D., Gelman, A., 2014. The no-U-turn sampler: adaptively
-  setting path lengths in Hamiltonian Monte Carlo.  J Mach Learn
-  Res. 15:1593-1623.
-
-Kristensen, K., Nielsen, A., Berg, C.W., Skaug, H., Bell, B.M.,
-  2016. TMB: Automatic differentiation and Laplace approximation.  J
-  Stat Softw. 70:21.
-
-Kristensen, K., 2017. TMB: General random effect model builder tool
-  inspired by ADMB. R package version 1.7.11.
-
-Monnahan, C.C., Thorson, J.T., Branch, T.A., 2017. Faster estimation of
-  Bayesian models in ecology using Hamiltonian Monte Carlo.  Methods in
-  Ecology and Evolution. 8:339-348.
-
-Monnahan C.C., Kristensen K. (2018). No-U-turn sampling for fast
- Bayesian inference in ADMB and TMB: Introducing the adnuts and
- tmbstan R packages PLoS ONE 13(5): e0197954.
- https://doi.org/10.1371/journal.pone.0197954
-
-Stan Development Team, 2016. Stan modeling language users guide and
-  reference manual, version 2.11.0.
-
-Stan Development Team, 2016. RStan: The R interface to Stan. R package
-version 2.14.1. http://mc-stan.org.
-}
-\keyword{internal}
diff --git a/man/as.tmbfit.Rd b/man/as.tmbfit.Rd
new file mode 100644
index 0000000..ddf7d79
--- /dev/null
+++ b/man/as.tmbfit.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sparse.R
+\name{as.tmbfit}
+\alias{as.tmbfit}
+\title{Construtor for tmbfit objects}
+\usage{
+as.tmbfit(x, mle, invf)
+}
+\arguments{
+\item{x}{A fitted MCMC object}
+
+\item{mle}{A list of MLE parameters}
+
+\item{invf}{The inverse function for the parameters}
+}
+\description{
+Construtor for tmbfit objects
+}
diff --git a/man/dot-rotate_space.Rd b/man/dot-rotate_space.Rd
new file mode 100644
index 0000000..41c0ab7
--- /dev/null
+++ b/man/dot-rotate_space.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{.rotate_space}
+\alias{.rotate_space}
+\title{Update algorithm for mass matrix.}
+\usage{
+.rotate_space(fn, gr, M, y.cur)
+}
+\arguments{
+\item{fn}{The current fn function.}
+
+\item{gr}{The current gr function}
+
+\item{M}{The new mass matrix}
+
+\item{y.cur}{The current parameter vector in unrotated (Y) space.}
+}
+\description{
+Update algorithm for mass matrix.
+}
diff --git a/man/get_post.Rd b/man/get_post.Rd
new file mode 100644
index 0000000..4a2d8e7
--- /dev/null
+++ b/man/get_post.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sparse.R
+\name{get_post}
+\alias{get_post}
+\title{Extract posterior samples from a tmbfit object}
+\usage{
+get_post(x, invf, parnames, array = FALSE)
+}
+\arguments{
+\item{x}{A fitted tmbfit object}
+
+\item{invf}{The inverse function to decorrelate the parameters}
+
+\item{parnames}{A vector of parameter names, excluding lp__}
+
+\item{array}{Whether to return a data.frame (default) or array
+which is used in constructing other objects downstream}
+}
+\description{
+Extract posterior samples from a tmbfit object
+}
diff --git a/man/sample_sparse_tmb.Rd b/man/sample_sparse_tmb.Rd
new file mode 100644
index 0000000..0dd8ad9
--- /dev/null
+++ b/man/sample_sparse_tmb.Rd
@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sparse.R
+\name{sample_sparse_tmb}
+\alias{sample_sparse_tmb}
+\title{Fit a TMB model using a sparse inverse mass matrix}
+\usage{
+sample_sparse_tmb(
+  obj,
+  iter,
+  warmup,
+  cores,
+  chains,
+  control = NULL,
+  seed = NULL
+)
+}
+\arguments{
+\item{obj}{The TMB object with random effects turned on and
+optimized}
+
+\item{iter}{Total iterations to run (warmup + sampling)}
+
+\item{warmup}{Total warmup iterations}
+
+\item{cores}{Number of parallel cores to use}
+
+\item{chains}{Number of chains}
+
+\item{control}{NUTS control list}
+
+\item{seed}{Random number seed}
+}
+\value{
+A fitted MCMC object of class 'adfit'
+}
+\description{
+Fit a TMB model using a sparse inverse mass matrix
+}