PRIORITY
* meta analysis -> akde -> overlap
* uncalibrate coati
* tplot: plot(...,axes=c('t','...'x')) or plot(...,axes=c('x','t')), possibly with 2 axes?, '-x'?
* lasso + outlie
* uere()<- ctmm calibrate with simultaneously fit RMS UERE
* p-value function for outlie() return object using v_max or CTMM object
* diffusion calculation with CIs (in summary)
  * check diffusion code  circulation + OU, OUF, OUO, OUf
  * diffusion units
  * integrate into summary()
* default: pHREML with range-resident models and REML with non-stationary models ?
* merge dt==0 times (and shrink errors) for variogram/correlogram
* mode.UD
* as.telemetry.telemetry (do nothing)
* bandwidth weights=TRUE dt>min(diff(t)) probably not subsetting data correctly (Pepper), need to "regularize" data for large dt, maybe this just happens with no error in model?
* summary.list needs to descend into lists recursively for uere objects
* simulate.telemetry with no CTMM object to simulate error only 2D/3D
* update optimizer to pracma::randortho
* check that the zero argument of optimizer is being used appropriately
* weighted mean() of UDs (Nina)
* overlap on mean()s of UDs (Ellie)
* likelihood error in Animove error turtle example
* mean method for ctmm objects
* residuals in vignette 'variogram' and 'error'
* precompute environments not carried over in Windows socket parallelization
* area/error CI when MLE=0 (min of normal upper from VAR)
* mode methods for ctmm and UD object (start with generic) (Erin?)
* akde on data.frame objects
* 1D ctmm summary doesn't make sense
* plotting GPS/ARGOS hybrid data without calibration makes GPS data invisible (VAR==0)
* ctmm.select run OUf.isotropic before OUf.anisotropic with OU/OUF.anisotropic + OUf.isotropic parameters
* ctmm.select optimize f of OUf in isolation first?
* ctmm.select optimize Omega of OUO in isolation first?
* prediction COV should be called VAR if length(axes)==1 or call everything COV
* optimize: was rank-1 update correlation preserving? was it local?
* 2D/3D fix argument for as.telemetry from number of satellites?
* variogram() error on lists
* error vignette should specify that tracking and calibration data have same location classes
* distribute cores from ctmm.select -> ctmm.fit optimizer
* mean, mode, median for ctmm and UD objects
* mean ctmm object
* fast plot of GPS-ARGOS hybrid data
* as.telemetry tibble->data.frame
* update buffalo data
* expose cex, lty, ... in plot.variogram
* combine two telemetry objects for same animal but different device (dyadic UEREs, match columns)
* compass speed plot -- or cycle argument for speeds()
* ctmm.fit -> id.parameters : force.error argument for when error is estimated to be 0
* error vignette - check back on variogram, correlogram
* reference Guillaume's paper at the beginning of the periodogram vignette

OPTIMIZER
* take multiple starting guesses---useful with ctmm.select phase I---useful with simplex method
* pNewton || pN
** (partial) pNewton-Nelder-Mead || pNNM
** (derivative-free) dfNewton-Nelder-Mead || dfNNM
  * make line search a function that returns the end point (and stuff)
  * initial pattern search for simplex, using expansion line search
  ** numerical 1st derivative at best estimate (pNNM)
  ** 1st derivative estimated from simpled (dfNNM)
  * line search from worst vertex to predicted estimate
* split-parameter optimization (for periodic parameters like angle) with funnelling error target
* Somehow optimizer can stop on stage 1 without proceeding to stage 2
* Stop on encapsulated?
* STAGE ?1/4? - pattern search
* try to fit Gamba with error
* mc.optimize cross curvature if queue allows space
* mc.optimize fill up queue with higher-order derivatives if further space allows
* mc.genD cross correlations are wrong


OVERLAP
* overlap needs to return DOFs in an overlap object - and then define a summary function for the overlap object
* overlaps' correlated errors

ERROR PAPER
* include error uncertainty in ctmm.fit output
* how does error change variogram DOF calculation?
* errors on x,y,z variograms
* errors on periodograms?
* exact 4x4 matrix inverse relations in PDsolve()
* UERE error propagation from calibration (do not use as prior!)
* email ARGOS about McClintock formula
* kGcluster with tpeqd projection suggestion
* predict can return VAR.xy for isotropic with UERE<4
* propagation of error uncertainty

PROJECTION
* check projection same for plot.telemetry
* projection() <- on all plottable objects + plot option
* project()<- ignores simulated/predicted velocities & covariances without headings & magnitudes
* error as.telemetry if user attempts lon-lat projection
* check latlon projection stop code against KT's input
* document projection projection<- dispatch methods

PARALLELIZATION
* parallelize akde.list & document overlap
* overlap() man example akde() first
* simulate nsim>1 with plapply
* parallel derivatives with upper/lower/etc.

MISC WORK
* should standardization be moved to ctmm.select?
* writeRaster, ... with lists of ctmm objects (when appropriate)
* when ctmm.fit estimates error==0 exactly, the uncertainty isn't propagated because error==FALSE
* simulate with emulate=TRUE
* significant digits function (MLE+CI) for summary functions
* akde/occurrence grid=UD/raster
* p-value test for trend terms using variance of mu_alt-mu_null
* significant digits argument to summary()
* functionalize detrend & retrend in simulate/predict
* predict.ctmm cov
* spline velocity mean function
* variogram discrepancy rank function
* color array -> spatial segregation
* plot axes=c('x','y') by default or axes=c('t','z')
* catch optimizer failure (last best) in global variable and finish optimize() with warning
* specify grid partially/totally in akde/occurrence
* pre-standardize length data in ctmm.fit & ctmm.loglike (if not already) - ctmm.prepare?
* times can be pre-standardized according to tau_velocity as well?
* opacity option for plot
* predict isotropic VAR.xy output
* SpatialPolygonsDataFrame.ctmm
* writeShapefile.ctmm
* define print.ctmm and see if that works
* simulate prior=FALSE
* remove mean from kalman smoother - detrend, then add back
* change all sapply to vapply
* make sure terms of kalman filter are sorted by |magnitude|
* resort all arrays to put time in last index
* FAQ entry on automated time format reading in as.telemetry breaking with R update
* non-range resident in vignette
* default labels not working for UD contours
* occurrence error option
* boundry warning in help(ctmm.fit)
* standardize x,y prior to likelihood calculation
* plot3D of 2D UD
* ltraj export
* export ellipse drawing function
* effective DOF for diffusion rate?

PERIODIC
* estimate periodicities in optimizer
* calculate explicit time link for periodicity? or estimate frequency?
* DOF[mean] should just be the stationary mean
* variogram on mean detrended data with CTMM argument
* simulate.telemetry (not pure ctmm)
* smoother (non-uniform u(t) and velocity vector)
* akde... slow exact and fast approximation or block toeplitz matrix (stationary Laplace approximation?)
* plot ctmm (fall back on kde code)
* vignette: periodogram, ctmm.select

VIGNETTES & EXAMPLES
* explain effective sample sizes (DOF of summary)

Aligned Krige UDs
pregridder composite dimension?

LOW HANGING FRUIT & Nagging issues
* add acf of calibration residuals to uere diagnostic
* sort.ctmm by IC argument
* simulate conditionally with t=t returns data and t times. predict does not. (lots of low-level changes here)
* move object coordinate x1,x2 lat-lon
* Bayesian=FALSE argument for simulate() leveraging emulate()
* project(telemetry) <- function
* check IID AICc formula for isotropic case
* change default gap skip in occurrence?
* rbind on telemetry objects
* grid fix regression - weight to coarse scale when appropriate
* detrend mean in correlogram function
* plot errors with HDOP & error model in CTMM
* rainbow.telemetry
* plot telemetry/UD with reverse axes (preserve x&y orientation)s
* give writeShapefile a default folder name, maybe also separate layers into different files
* compass plot
* unit Environment on plots for points()
* individualized projections
* 1-point azimuthal equidistant projection quick option
* overload covm * / to apply to area & matrix
* str.ctmm, str.telemetry (show, print too?)
* points methods units environment
* have summary return a warning on occurrence distributions
* variogram.list <- mean-lapply-variogram
* occurrence.list
  + need to choose/enforce the same time steps?
  + weight by period of data?
* mean.UD on occurrence objects; +1 DOF
* simulate.UD
* Bayesian simulate.ctmm models (not data)
* krige velocities, activity plot
* put overlap in vignette after publication
* predict plot with transparency proportional to uncertainty (Google Earth?)
* mean.ctmm: list -> iid

generalize Gaussian area?
  UDs for both mean and population
  homerange(method=normal)
time/tube plot
periodic plot.ctmm

level.UD of summary UD/ctmm return Inf
  test p-value against sample size

summary on z-axis ctmm.fit might be buggy

sort.ctmm
sort.list
name.ctmm
name.generic

zoom.telemetry x,y offset clicker

global variable for units of current plot?

proj4: email about making north up
plot N vector in corner

dogleg splines
  degree = 1,2?
  migration = times?
  settlement = times?

model option vignette
IID, OU, OUF, (an)isotropic table in vignette

fixed circulation option?
Fixed period parameters during fitting?

3d periodogram, ctmm.fit, krige, kde

simulate nsim in both unconditioned and conditionedb by storing all objects
multiple conditional simulations loop
More efficient sampler/smoother:
store propagator results for all time-lags
store forecast/concurrent estimates
call vectorized RNG

Include telemetry errors in unconditional simulation?
krige -> predict

cross variogram

Newton-Raphson iteration at end of ctmm.fit for solution check on optim

AKDE vignette: DOF.H, DOF.mu

plot options: cex, type, abrv

AKDE with errors, weights
akde with user-specified grid (and projection)

residual acf versus data acf with correct confidence intervals for no autocorrelation null hypothesis

multi-scale variogram bias correction
variogram & periodogram: coarsening window method to avoid all bias

population fitting without correlation
population fit of list
population akde of lists

Lapack GE solve -> Lapack PO/SY solve.

profile CIs

contour labels in plot ctmm

Email ks author about inaccuracy

Repeated times in variograms -- account for as error -- fix cap

dplyr, rolling for loops

fixed mean parameter for boundary issues

HIERARCHICAL___________
* stochastic gradient Langevin dynamics
* proximal methods of optimization
